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Gao Y, Li Z, Liu M, Tian J. Patient-level and trial-level data meta-analyses. Lancet 2024; 404:243. [PMID: 39033008 DOI: 10.1016/s0140-6736(24)00864-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 04/22/2024] [Indexed: 07/23/2024]
Affiliation(s)
- Ya Gao
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Zhifan Li
- The First Clinical Medical College of Lanzhou University, Lanzhou, China
| | - Ming Liu
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Jinhui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China.
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Wei L, Butterly E, Rodríguez Pérez J, Chowdhury A, Shemilt R, Hanlon P, McAllister D. Description of subgroup reporting in clinical trials of chronic diseases: a meta-epidemiological study. BMJ Open 2024; 14:e081315. [PMID: 38908852 PMCID: PMC11328666 DOI: 10.1136/bmjopen-2023-081315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/24/2024] Open
Abstract
INTRODUCTION In trials, subgroup analyses are used to examine whether treatment effects differ by important patient characteristics. However, which subgroups are most commonly reported has not been comprehensively described. DESIGN AND SETTINGS Using a set of trials identified from the US clinical trials register (ClinicalTrials.gov), we describe every reported subgroup for a range of conditions and drug classes. METHODS We obtained trial characteristics from ClinicalTrials.gov via the Aggregate Analysis of ClinicalTrials.gov database. We subsequently obtained all corresponding PubMed-indexed papers and screened these for subgroup reporting. Tables and text for reported subgroups were extracted and standardised using Medical Subject Headings and WHO Anatomical Therapeutic Chemical codes. Via logistic and Poisson regression models we identified independent predictors of result reporting (any vs none) and subgroup reporting (any vs none and counts). We then summarised subgroup reporting by index condition and presented all subgroups for all trials via a web-based interactive heatmap (https://ihwph-hehta.shinyapps.io/subgroup_reporting_app/). RESULTS Among 2235 eligible trials, 23% (524 trials) reported subgroups. Follow-up time (OR, 95%CI: 1.13, 1.04-1.24), enrolment (per 10-fold increment, 3.48, 2.25-5.47), trial starting year (1.07, 1.03-1.11) and specific index conditions (eg, hypercholesterolaemia, hypertension, taking asthma as the reference, OR ranged from 0.15 to 10.44), predicted reporting, sponsoring source and number of arms did not. Results were similar on modelling any result reporting (except number of arms, 1.42, 1.15-1.74) and the total number of subgroups. Age (51%), gender (45%), racial group (28%) were the most frequently reported subgroups. Characteristics related to the index condition (severity/duration/types etc) were frequently reported (eg, 69% of myocardial infarction trials reported on its severity/duration/types). However, reporting on comorbidity/frailty (five trials) and mental health (four trials) was rare. CONCLUSION Other than age, sex, race ethnicity or geographic location and characteristics related to the index condition, information on variation in treatment effects is sparse. PROSPERO REGISTRATION NUMBER CRD42018048202.
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Affiliation(s)
- Lili Wei
- University of Glasgow School of Health and Wellbeing, Glasgow, UK
| | - Elaine Butterly
- University of Glasgow School of Health and Wellbeing, Glasgow, UK
| | | | | | - Richard Shemilt
- University of Glasgow School of Health and Wellbeing, Glasgow, UK
| | - Peter Hanlon
- University of Glasgow School of Health and Wellbeing, Glasgow, UK
| | - David McAllister
- University of Glasgow School of Health and Wellbeing, Glasgow, UK
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3
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Wallach JD, Glick L, Gueorguieva R, O’Malley SS. Evidence of subgroup differences in meta-analyses evaluating medications for alcohol use disorder: An umbrella review. ALCOHOL, CLINICAL & EXPERIMENTAL RESEARCH 2024; 48:5-15. [PMID: 38102794 PMCID: PMC10841726 DOI: 10.1111/acer.15229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/13/2023] [Accepted: 11/13/2023] [Indexed: 12/17/2023]
Abstract
Randomized controlled trials (RCTs) evaluating medications for alcohol use disorder (AUD) often examine heterogeneity of treatment effects through subgroup analyses that contrast effect estimates in groups of patients across individual demographic, clinical, and study design-related characteristics. However, these analyses are often not prespecified or adequately powered, highlighting the potential role of subgroup analyses in meta-analysis. Here, we conducted an umbrella review (i.e., a systematic review of meta-analyses) to determine the range and characteristics of reported subgroup analyses in meta-analyses of AUD medications. We searched PubMed to identify meta-analyses of RCTs evaluating medications for the management of AUD, alcohol abuse, or alcohol dependence in adults. We sought studies that measured drinking-related outcomes; quality of life, function, and rates of mortality; adverse events; and dropout. We considered meta-analyses that reported the results from formal subgroup analyses (comparing the summary effects across subgroup levels); summary effect estimates stratified across subgroup levels; and meta-regression, regression, or correlation-based subgroup analyses. We analyzed nine meta-analyses that included 61 formal subgroup analyses (median = 6 per meta-analysis), of which 33 (54%) were based on baseline participant-level and 28 (46%) were based on trial-level characteristics. Of the 58 subgroup analyses with either a p-value from a subgroup test or a statement by the authors that the subgroup analyses were not statistically significant, eight (14%) were statistically significant at the p < 0.05 level. Twelve meta-analyses reported the results of 102 meta-regression analyses, of which 25 (25%) identified statistically significant predictors of the relevant outcome of interest; nine (9%) were based on baseline participant-level and 93 (91%) were based on trial characteristics. Subgroup analyses across meta-analyses of AUD medications often focus on study-level characteristics, which may not be as clinically informative as subgroup analyses based on participant-level characteristics. Opportunities exist for future meta-analyses to standardize their subgroup methodology, focus on more clinically informative participant-level characteristics, and use predictive approaches to account for multiple relevant variables.
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Affiliation(s)
- Joshua D. Wallach
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Laura Glick
- Department of Internal Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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4
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Estimating the prevalence of discrepancies between study registrations and publications: a systematic review and meta-analyses. BMJ Open 2023; 13:e076264. [PMID: 37793922 PMCID: PMC10551944 DOI: 10.1136/bmjopen-2023-076264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 06/28/2023] [Indexed: 10/06/2023] Open
Abstract
OBJECTIVES Prospectively registering study plans in a permanent time-stamped and publicly accessible document is becoming more common across disciplines and aims to reduce risk of bias and make risk of bias transparent. Selective reporting persists, however, when researchers deviate from their registered plans without disclosure. This systematic review aimed to estimate the prevalence of undisclosed discrepancies between prospectively registered study plans and their associated publication. We further aimed to identify the research disciplines where these discrepancies have been observed, whether interventions to reduce discrepancies have been conducted, and gaps in the literature. DESIGN Systematic review and meta-analyses. DATA SOURCES Scopus and Web of Knowledge, published up to 15 December 2019. ELIGIBILITY CRITERIA Articles that included quantitative data about discrepancies between registrations or study protocols and their associated publications. DATA EXTRACTION AND SYNTHESIS Each included article was independently coded by two reviewers using a coding form designed for this review (osf.io/728ys). We used random-effects meta-analyses to synthesise the results. RESULTS We reviewed k=89 articles, which included k=70 that reported on primary outcome discrepancies from n=6314 studies and, k=22 that reported on secondary outcome discrepancies from n=1436 studies. Meta-analyses indicated that between 29% and 37% (95% CI) of studies contained at least one primary outcome discrepancy and between 50% and 75% (95% CI) contained at least one secondary outcome discrepancy. Almost all articles assessed clinical literature, and there was considerable heterogeneity. We identified only one article that attempted to correct discrepancies. CONCLUSIONS Many articles did not include information on whether discrepancies were disclosed, which version of a registration they compared publications to and whether the registration was prospective. Thus, our estimates represent discrepancies broadly, rather than our target of undisclosed discrepancies between prospectively registered study plans and their associated publications. Discrepancies are common and reduce the trustworthiness of medical research. Interventions to reduce discrepancies could prove valuable. REGISTRATION osf.io/ktmdg. Protocol amendments are listed in online supplemental material A.
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Free C, Palmer MJ, Potter K, McCarthy OL, Jerome L, Berendes S, Gubijev A, Knight M, Jamal Z, Dhaliwal F, Carpenter JR, Morris TP, Edwards P, French R, Macgregor L, Turner KME, Baraitser P, Hickson FCI, Wellings K, Roberts I, Bailey JV, Hart G, Michie S, Clayton T, Devries K. Behavioural intervention to reduce sexually transmitted infections in people aged 16–24 years in the UK: the safetxt RCT. PUBLIC HEALTH RESEARCH 2023. [DOI: 10.3310/dane8826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Background
The prevalence of genital chlamydia and gonorrhoea is higher in the 16–24 years age group than those in other age group. With users, we developed the theory-based safetxt intervention to reduce sexually transmitted infections.
Objectives
To establish the effect of the safetxt intervention on the incidence of chlamydia/gonorrhoea infection at 1 year.
Design
A parallel-group, individual-level, randomised superiority trial in which care providers and outcome assessors were blinded to allocation.
Setting
Recruitment was from 92 UK sexual health clinics.
Participants
Inclusion criteria were a positive chlamydia or gonorrhoea test result, diagnosis of non-specific urethritis or treatment started for chlamydia/gonorrhoea/non-specific urethritis in the last 2 weeks; owning a personal mobile phone; and being aged 16–24 years.
Allocation
Remote computer-based randomisation with an automated link to the messaging system delivering intervention or control group messages.
Intervention
The safetxt intervention was designed to reduce sexually transmitted infection by increasing partner notification, condom use and sexually transmitted infection testing before sex with new partners. It employed educational, enabling and incentivising content delivered by 42–79 text messages over 1 year, tailored according to type of infection, gender and sexuality.
Comparator
A monthly message regarding trial participation.
Main outcomes
The primary outcome was the incidence of chlamydia and gonorrhoea infection at 12 months, assessed using nucleic acid amplification tests. Secondary outcomes at 1 and 12 months included self-reported partner notification, condom use and sexually transmitted infection testing prior to sex with new partner(s).
Results
Between 1 April 2016 and 23 November 2018, we assessed 20,476 people for eligibility and consented and randomised 6248 participants, allocating 3123 to the safetxt intervention and 3125 to the control. Primary outcome data were available for 4675 (74.8%) participants. The incidence of chlamydia/gonorrhoea infection was 22.2% (693/3123) in the intervention group and 20.3% (633/3125) in the control group (odds ratio 1.13, 95% confidence interval 0.98 to 1.31). There was no evidence of heterogeneity in any of the prespecified subgroups. Partner notification was 85.6% in the intervention group and 84.0% in the control group (odds ratio 1.14, 95% confidence interval 0.99 to 1.33). At 12 months, condom use at last sex was 33.8% in the intervention group and 31.2% in the control group (odds ratio 1.14, 95% confidence interval 1.01 to 1.28) and condom use at first sex with most recent new partner was 54.4% in the intervention group and 48.7% in the control group (odds ratio 1.27, 95% confidence interval 1.11 to 1.45). Testing before sex with a new partner was 39.5% in the intervention group and 40.9% in the control group (odds ratio 0.95, 95% confidence interval 0.82 to 1.10). Having two or more partners since joining the trial was 56.9% in the intervention group and 54.8% in the control group (odds ratio 1.11, 95% confidence interval 1.00 to 1.24) and having sex with someone new since joining the trial was 69.7% in the intervention group and 67.4% in the control group (odds ratio 1.13, 95% confidence interval 1.00 to 1.28). There were no differences in safety outcomes. Additional sensitivity and per-protocol analyses showed similar results.
Limitations
Our understanding of the mechanism of action for the unanticipated effects is limited.
Conclusions
The safetxt intervention did not reduce chlamydia and gonorrhoea infections, with slightly more infections in the intervention group. The intervention increased condom use but also increased the number of partners and new partners. Randomised controlled trials are essential for evaluating health communication interventions, which can have unanticipated effects.
Future work
Randomised controlled trials evaluating novel interventions in this complex area are needed.
Trial registration
This trial is registered as ISRCTN64390461.
Funding
This project was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme and will be published in full in Public Health Research; Vol. 11, No. 1. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Caroline Free
- Clinical Trials Unit, Department of Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Melissa J Palmer
- Clinical Trials Unit, Department of Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Kimberley Potter
- Clinical Trials Unit, Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Ona L McCarthy
- Clinical Trials Unit, Department of Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Lauren Jerome
- Clinical Trials Unit, Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Sima Berendes
- Clinical Trials Unit, Department of Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Anasztazia Gubijev
- Clinical Trials Unit, Department of Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Megan Knight
- Clinical Trials Unit, Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Zahra Jamal
- Clinical Trials Unit, Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Farandeep Dhaliwal
- Clinical Trials Unit, Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - James R Carpenter
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Tim P Morris
- Medical Research Council Clinical Trials Unit, London, UK
| | - Phil Edwards
- Clinical Trials Unit, Department of Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Rebecca French
- Department of Social and Environmental Health Research, London School of Hygiene & Tropical Medicine, London, UK
| | - Louis Macgregor
- Bristol Veterinary School, University of Bristol, Bristol, UK
| | - Katy ME Turner
- Bristol Veterinary School, University of Bristol, Bristol, UK
| | | | - Ford CI Hickson
- Sigma Research, London School of Hygiene & Tropical Medicine, London, UK
| | - Kaye Wellings
- Department of Social and Environmental Health Research, London School of Hygiene & Tropical Medicine, London, UK
| | - Ian Roberts
- Clinical Trials Unit, Department of Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Julia V Bailey
- eHealth Unit, Research Department of Primary Care and Population Health, University College London, London, UK
| | - Graham Hart
- Department of Infection and Population Health, University College London, London, UK
| | - Susan Michie
- Centre for Outcomes Research and Effectiveness, University College London, London, UK
| | - Tim Clayton
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Karen Devries
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
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Henriksen M, Nielsen SM, Christensen R, Kristensen LE, Bliddal H, Bartholdy C, Boesen M, Ellegaard K, Hunter DJ, Altman R, Bandak E. Who are likely to benefit from the Good Life with osteoArthritis in Denmark (GLAD) exercise and education program? An effect modifier analysis of a randomised controlled trial. Osteoarthritis Cartilage 2023; 31:106-114. [PMID: 36089229 DOI: 10.1016/j.joca.2022.09.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/16/2022] [Accepted: 09/01/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To identify contextual factors that modify the treatment effect of the 'Good Life with osteoArthritis in Denmark' (GLAD) exercise and education programme compared to open-label placebo (OLP) on knee pain in individuals with knee osteoarthritis (OA). METHODS Secondary effect modifier analysis of a randomised controlled trial. 206 participants with symptomatic and radiographic knee OA were randomised to either the 8-week GLAD programme (n = 102) or OLP given as 4 intra-articular saline injections over 8 weeks (n = 104). The primary outcome was change from baseline to week 9 in the Knee injury and Osteoarthritis Outcome Score questionnaire (KOOS) pain subscale (range 0 (worst) to 100 (best)). Subgroups were created based on baseline information: BMI, swollen study knee, bilateral radiographic knee OA, sports participation as a young adult, sex, median age, a priori treatment preference, regular use of analgesics (NSAIDs or paracetamol), radiographic disease severity, and presence of constant or intermittent pain. RESULTS Participants who reported use of analgesics at baseline seem to benefit from the GLAD programme over OLP (subgroup contrast: 10.3 KOOS pain points (95% CI 3.0 to 17.6)). Participants with constant pain at baseline also seem to benefit from GLAD over OLP (subgroup contrast: 10.0 points (95% CI 2.8 to 17.2)). CONCLUSIONS These results imply that patients who take analgesics or report constant knee pain, GLAD seems to yield clinically relevant benefits on knee pain when compared to OLP. The results support a stratified recommendation of GLAD as management of knee OA. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT03843931. EudraCT number 2019-000809-71.
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Affiliation(s)
- M Henriksen
- The Parker Institute, Copenhagen University Hospital Bispebjerg-Frederiksberg, Denmark.
| | - S M Nielsen
- The Parker Institute, Copenhagen University Hospital Bispebjerg-Frederiksberg, Denmark; Research Unit of Rheumatology, Department of Clinical Research, University of Southern Denmark, Odense University Hospital, Denmark
| | - R Christensen
- The Parker Institute, Copenhagen University Hospital Bispebjerg-Frederiksberg, Denmark; Research Unit of Rheumatology, Department of Clinical Research, University of Southern Denmark, Odense University Hospital, Denmark
| | - L E Kristensen
- The Parker Institute, Copenhagen University Hospital Bispebjerg-Frederiksberg, Denmark
| | - H Bliddal
- The Parker Institute, Copenhagen University Hospital Bispebjerg-Frederiksberg, Denmark
| | - C Bartholdy
- The Parker Institute, Copenhagen University Hospital Bispebjerg-Frederiksberg, Denmark
| | - M Boesen
- The Parker Institute, Copenhagen University Hospital Bispebjerg-Frederiksberg, Denmark
| | - K Ellegaard
- The Parker Institute, Copenhagen University Hospital Bispebjerg-Frederiksberg, Denmark
| | - D J Hunter
- Institute of Bone and Joint Research, Kolling Institute of Medical Research, The University of Sydney, Australia; Department of Rheumatology, Royal North Shore Hospital, Sydney, Australia
| | - R Altman
- Ronald Reagan UCLA Medical Center, Los Angeles, CA, USA
| | - E Bandak
- The Parker Institute, Copenhagen University Hospital Bispebjerg-Frederiksberg, Denmark
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Fang M, Xu C, Ma L, Sun Y, Zhou X, Deng J, Liu X. No sex difference was found in the safety and efficacy of intravenous alteplase before endovascular therapy. Front Neurol 2022; 13:989166. [DOI: 10.3389/fneur.2022.989166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 09/01/2022] [Indexed: 11/11/2022] Open
Abstract
Background and purposePrior studies on sex disparities were post-hoc analyses, had limited treatment modalities, and had controversial findings. Our study aimed to examine whether sex difference modifies the effect of intravenous alteplase before endovascular therapy.MethodsWe conducted a multicenter prospective cohort study of 850 eligible patients with acute ischemic stroke who underwent endovascular therapy. A propensity score was utilized as a covariate to achieve approximate randomization of alteplase pretreatment. The baseline characteristics of women and men were compared. Logistic regression with interaction terms, adjusted for potential confounders, was used to investigate the effect of sex on the prognosis of bridging therapy.ResultsIn comparison to men, women were older [78.00 (70.00–84.00) vs. 67 (61.00–74.00), P < 0.001], had more atrial fibrillation (61.4 vs. 35.2%, P < 0.001), had a lower ASPECTS [10.00 (8.00–10.00) vs. 10 (9.00–10.00), P = 0.0047], and had a higher NIHSS score [17.00 (14.00–20.00) vs. 16 (13.00–19.00), P = 0.005]. Women tended to receive less bridging therapy (26.3 vs. 33%, P = 0.043) and more retrieval attempts [2.00 (1.00–2.00) vs. 1 (1.00–2.00), P = 0.026]. There was no sex difference in functional independence at 90 days after bridging therapy (OR 0.968, 95% CI 0.575–1.63), whereas men benefited more after EVT alone (OR 0.654, 95% CI 0.456–0.937). There were no sex-treatment interactions observed regardless of the location of the occlusion. There were no significant sex differences in all safety outcomes.ConclusionOur study could not confirm that sex modifies the treatment effect of intravenous alteplase before endovascular therapy. At the same time, we advocate for women to seek timely medical treatment.
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Free C, Palmer MJ, McCarthy OL, Jerome L, Berendes S, Knight M, Carpenter JR, Morris TP, Jamal Z, Dhaliwal F, French RS, Hickson FCI, Gubijev A, Wellings K, Baraitser P, Roberts I, Bailey JV, Clayton T, Devries K, Edwards P, Hart G, Michie S, Macgregor L, Turner KME, Potter K. Effectiveness of a behavioural intervention delivered by text messages (safetxt) on sexually transmitted reinfections in people aged 16-24 years: randomised controlled trial. BMJ 2022; 378:e070351. [PMID: 36170988 PMCID: PMC9516322 DOI: 10.1136/bmj-2022-070351] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To quantify the effects of a series of text messages (safetxt) delivered in the community on incidence of chlamydia and gonorrhoea reinfection at one year in people aged 16-24 years. DESIGN Parallel group randomised controlled trial. SETTING 92 sexual health clinics in the United Kingdom. PARTICIPANTS People aged 16-24 years with a diagnosis of, or treatment for, chlamydia, gonorrhoea, or non-specific urethritis in the past two weeks who owned a mobile phone. INTERVENTIONS 3123 participants assigned to the safetxt intervention received a series of text messages to improve sex behaviours: four texts daily for days 1-3, one or two daily for days 4-28, two or three weekly for month 2, and 2-5 monthly for months 3-12. 3125 control participants received a monthly text message for one year asking for any change to postal or email address. It was hypothesised that safetxt would reduce the risk of chlamydia and gonorrhoea reinfection at one year by improving three key safer sex behaviours: partner notification at one month, condom use, and sexually transmitted infection testing before unprotected sex with a new partner. Care providers and outcome assessors were blind to allocation. MAIN OUTCOME MEASURES The primary outcome was the cumulative incidence of chlamydia or gonorrhoea reinfection at one year, assessed by nucleic acid amplification tests. Safety outcomes were self-reported road traffic incidents and partner violence. All analyses were by intention to treat. RESULTS 6248 of 20 476 people assessed for eligibility between 1 April 2016 and 23 November 2018 were randomised. Primary outcome data were available for 4675/6248 (74.8%). At one year, the cumulative incidence of chlamydia or gonorrhoea reinfection was 22.2% (693/3123) in the safetxt arm versus 20.3% (633/3125) in the control arm (odds ratio 1.13, 95% confidence interval 0.98 to 1.31). The number needed to harm was 64 (95% confidence interval number needed to benefit 334 to ∞ to number needed to harm 24) The risk of road traffic incidents and partner violence was similar between the groups. CONCLUSIONS The safetxt intervention did not reduce chlamydia and gonorrhoea reinfections at one year in people aged 16-24 years. More reinfections occurred in the safetxt group. The results highlight the need for rigorous evaluation of health communication interventions. TRIAL REGISTRATION ISRCTN registry ISRCTN64390461.
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Affiliation(s)
- Caroline Free
- Clinical Trials Unit, Department of Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Melissa J Palmer
- Clinical Trials Unit, Department of Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Ona L McCarthy
- Clinical Trials Unit, Department of Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Lauren Jerome
- Clinical Trials Unit, Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Sima Berendes
- Clinical Trials Unit, Department of Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Megan Knight
- Clinical Trials Unit, Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - James R Carpenter
- Clinical Trials Unit, Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Zahra Jamal
- Clinical Trials Unit, Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Farandeep Dhaliwal
- Clinical Trials Unit, Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Rebecca S French
- Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Anasztazia Gubijev
- Clinical Trials Unit, Department of Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Kaye Wellings
- Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Ian Roberts
- Clinical Trials Unit, Department of Population Health, London School of Hygiene and Tropical Medicine, London, UK
- Clinical Trials Unit, Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Julia V Bailey
- eHealth Unit, Research Department of Primary care and Population Health, University College London, London, UK
| | - Tim Clayton
- Clinical Trials Unit, Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Karen Devries
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
| | - Phil Edwards
- Clinical Trials Unit, Department of Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Graham Hart
- Department of Infection and Population Health, University College London, London, UK
| | - Susan Michie
- Centre for Outcomes Research and Effectiveness, University College London, London, UK
| | - Louis Macgregor
- Bristol Veterinary School, University of Bristol, Bristol, UK
| | - Katy M E Turner
- Bristol Veterinary School, University of Bristol, Bristol, UK
| | - Kimberley Potter
- Clinical Trials Unit, Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
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Wolf JM, Koopmeiners JS, Vock DM. A permutation procedure to detect heterogeneous treatments effects in randomized clinical trials while controlling the type I error rate. Clin Trials 2022; 19:512-521. [PMID: 35531765 DOI: 10.1177/17407745221095855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND/AIMS Secondary analyses of randomized clinical trials often seek to identify subgroups with differential treatment effects. These discoveries can help guide individual treatment decisions based on patient characteristics and identify populations for which additional treatments are needed. Traditional analyses require researchers to pre-specify potential subgroups to reduce the risk of reporting spurious results. There is a need for methods that can detect such subgroups without a priori specification while allowing researchers to control the probability of falsely detecting heterogeneous subgroups when treatment effects are uniform across the study population. METHODS We propose a permutation procedure for tuning parameter selection that allows for type I error control when testing for heterogeneous treatment effects framed within the Virtual Twins procedure for subgroup identification. We verify that the type I error rate can be controlled at the nominal rate and investigate the power for detecting heterogeneous effects when present through extensive simulation studies. We apply our method to a secondary analysis of data from a randomized trial of very low nicotine content cigarettes. RESULTS In the absence of type I error control, the observed type I error rate for Virtual Twins was between 99% and 100%. In contrast, models tuned via the proposed permutation were able to control the type I error rate and detect heterogeneous effects when present. An application of our approach to a recently completed trial of very low nicotine content cigarettes identified several variables with potentially heterogeneous treatment effects. CONCLUSIONS The proposed permutation procedure allows researchers to engage in secondary analyses of clinical trials for treatment effect heterogeneity while maintaining the type I error rate without pre-specifying subgroups.
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Affiliation(s)
- Jack M Wolf
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Joseph S Koopmeiners
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - David M Vock
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
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Cintron DW, Adler NE, Gottlieb LM, Hagan E, Tan ML, Vlahov D, Glymour MM, Matthay EC. Heterogeneous treatment effects in social policy studies: An assessment of contemporary articles in the health and social sciences. Ann Epidemiol 2022; 70:79-88. [PMID: 35483641 DOI: 10.1016/j.annepidem.2022.04.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 04/13/2022] [Accepted: 04/20/2022] [Indexed: 11/01/2022]
Abstract
PURPOSE . Social policies are important determinants of population health but may have varying effects on subgroups of people. Evaluating heterogeneous treatment effects (HTEs) of social policies is critical to determine how social policies will affect health inequities. Methods for evaluating HTEs are not standardized. Little is known about how often and by what methods HTEs are assessed in social policy and health research. METHODS . A sample of 55 articles from 2019 on the health effects of social policies were evaluated for frequency of reporting HTEs; for what subgroupings HTEs were reported; frequency of a priori specification of intent to assess HTEs; and methods used for assessing HTEs. RESULTS . 24 (44%) studies described some form of HTE assessment, including by age, gender, education, race/ethnicity, and/or geography. Among studies assessing HTEs, 63% specified HTE assessment a priori, and most (71%) used descriptive methods such as stratification; 21% used statistical tests (e.g., interaction terms in a regression); and no studies used data-driven algorithms. CONCLUSIONS . Although understanding HTEs could enhance policy and practice-based efforts to reduce inequities, it is not routine research practice. Increased evaluation of HTEs across relevant subgroups is needed.
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Affiliation(s)
- Dakota W Cintron
- Center for Health and Community, University of California, San Francisco, 3333 California St., Suite 465, Campus Box 0844, San Francisco, CA, 94143-0844, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, 2nd Floor, Campus Box 0560, San Francisco, CA, 94143, USA
| | - Nancy E Adler
- Center for Health and Community, University of California, San Francisco, 3333 California St., Suite 465, Campus Box 0844, San Francisco, CA, 94143-0844, USA
| | - Laura M Gottlieb
- Center for Health and Community, University of California, San Francisco, 3333 California St., Suite 465, Campus Box 0844, San Francisco, CA, 94143-0844, USA
| | - Erin Hagan
- Center for Health and Community, University of California, San Francisco, 3333 California St., Suite 465, Campus Box 0844, San Francisco, CA, 94143-0844, USA
| | - May Lynn Tan
- Center for Health and Community, University of California, San Francisco, 3333 California St., Suite 465, Campus Box 0844, San Francisco, CA, 94143-0844, USA
| | - David Vlahov
- Yale School of Nursing at Yale University, 400 West Campus Drive, Room 32306, Orange, CT, 06477, USA
| | - M Maria Glymour
- Center for Health and Community, University of California, San Francisco, 3333 California St., Suite 465, Campus Box 0844, San Francisco, CA, 94143-0844, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, 2nd Floor, Campus Box 0560, San Francisco, CA, 94143, USA
| | - Ellicott C Matthay
- Center for Health and Community, University of California, San Francisco, 3333 California St., Suite 465, Campus Box 0844, San Francisco, CA, 94143-0844, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, 2nd Floor, Campus Box 0560, San Francisco, CA, 94143, USA.
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11
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Emary PC, Stuber KJ, Mbuagbaw L, Oremus M, Nolet PS, Nash JV, Bauman CA, Ciraco C, Couban RJ, Busse JW. Risk of bias in chiropractic mixed methods research: a secondary analysis of a meta-epidemiological review. THE JOURNAL OF THE CANADIAN CHIROPRACTIC ASSOCIATION 2022; 66:7-20. [PMID: 35655699 PMCID: PMC9103633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To examine the risk of bias in chiropractic mixed methods research. METHODS We performed a secondary analysis of a meta-epidemiological review of chiropractic mixed methods studies. We assessed risk of bias with the Mixed Methods Appraisal Tool (MMAT) and used generalized estimating equations to explore factors associated with risk of bias. RESULTS Among 55 eligible studies, a mean of 62% (6.8 [2.3]/11) of MMAT items were fulfilled. In our adjusted analysis, studies published since 2010 versus pre-2010 (adjusted odds ratio [aOR] = 2.26; 95% confidence interval [CI], 1.39 to 3.68) and those published in journals with an impact factor versus no impact factor (aOR = 2.21; 95% CI, 1.33 to 3.68) were associated with lower risk of bias. CONCLUSION Our findings suggest opportunities for improvement in the quality of conduct among published chiropractic mixed methods studies. Author compliance with the MMAT criteria may reduce methodological bias in future mixed methods research.
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Affiliation(s)
- Peter C. Emary
- Department of Health Research Methods, Evidence and Impact, McMaster University
- Chiropractic Department, D’Youville University
- Private Practice
| | - Kent J. Stuber
- Department of Graduate Education and Research, Canadian Memorial Chiropractic College
| | - Lawrence Mbuagbaw
- Department of Health Research Methods, Evidence and Impact, McMaster University
- Biostatistics Unit, Father Sean O’Sullivan Research Centre, St. Joseph’s Healthcare-Hamilton
- Centre for the Development of Best Practices in Health, Yaundé, Cameroon
- Division of Global Health, Stellenbosch University, South Africa
| | - Mark Oremus
- Department of Health Research Methods, Evidence and Impact, McMaster University
- School of Public Health Sciences, University of Waterloo
| | - Paul S. Nolet
- Department of Graduate Education and Research, Canadian Memorial Chiropractic College
- Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | | | - Craig A. Bauman
- Department of Family Medicine, McMaster University
- The Centre for Family Medicine Family Health Team, Kitchener, Ontario
| | | | - Rachel J. Couban
- Michael G. DeGroote National Pain Centre, McMaster University, Hamilton, Ontario, Canada
| | - Jason W. Busse
- Department of Health Research Methods, Evidence and Impact, McMaster University
- Department of Anesthesia, McMaster University
- Michael G. DeGroote National Pain Centre, McMaster University, Hamilton, Ontario, Canada
- Chronic Pain Centre of Excellence for Canadian Veterans, Hamilton, Ontario, Canada
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12
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Shields GE, Wilberforce M, Clarkson P, Farragher T, Verma A, Davies LM. Factors Limiting Subgroup Analysis in Cost-Effectiveness Analysis and a Call for Transparency. PHARMACOECONOMICS 2022; 40:149-156. [PMID: 34713422 PMCID: PMC8553493 DOI: 10.1007/s40273-021-01108-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/18/2021] [Indexed: 05/03/2023]
Abstract
The use of population averages in cost-effectiveness analysis may hide important differences across subgroups, potentially resulting in suboptimal resource allocation, reduced population health and/or increased health inequalities. We discuss the factors that limit subgroup analysis in cost-effectiveness analysis and propose more thorough and transparent reporting. There are many issues that may limit whether subgroup analysis can be robustly included in cost-effectiveness analysis, including challenges with prespecifying and justifying subgroup analysis, identifying subgroups that can be implemented (identified and targeted) in practice, resource and data requirements, and statistical and ethical concerns. These affect every stage of the design, development and reporting of cost-effectiveness analyses. It may not always be possible to include and report relevant subgroups in cost effectiveness, e.g. due to data limitations. Reasons for not conducting subgroup analysis may be heterogeneous, and the consequences of not acknowledging patient heterogeneity can be substantial. We recommend that when potentially relevant subgroups have not been included in a cost-effectiveness analysis, authors report this and discuss their rationale and the limitations of this. Greater transparency of subgroup reporting should provide a starting point to overcoming these challenges in future research.
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Affiliation(s)
- Gemma E Shields
- Division of Population Health, Health Services Research, and Primary Care, Faculty of Biology, Medicine and Health, Manchester Centre for Health Economics, School of Health Sciences, University of Manchester, Manchester, UK.
| | - Mark Wilberforce
- Social Policy Research Unit, Department of Social Policy and Social Work, University of York, York, UK
| | - Paul Clarkson
- Social Care and Society, Division of Population Health, Health Services Research, and Primary Care, Faculty of Biology, Medicine and Health, School of Health Sciences, University of Manchester, Manchester, UK
| | - Tracey Farragher
- The Epidemiology and Public Health Group (EPHG), Division of Population Health, Health Services Research, and Primary Care, Faculty of Biology, Medicine and Health, School of Health Sciences, University of Manchester, Manchester, UK
| | - Arpana Verma
- The Epidemiology and Public Health Group (EPHG), Division of Population Health, Health Services Research, and Primary Care, Faculty of Biology, Medicine and Health, School of Health Sciences, University of Manchester, Manchester, UK
- Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - Linda M Davies
- Division of Population Health, Health Services Research, and Primary Care, Faculty of Biology, Medicine and Health, Manchester Centre for Health Economics, School of Health Sciences, University of Manchester, Manchester, UK
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Taji Heravi A, Gryaznov D, Schandelmaier S, Kasenda B, Briel M. Evaluation of Planned Subgroup Analysis in Protocols of Randomized Clinical Trials. JAMA Netw Open 2021; 4:e2131503. [PMID: 34705015 PMCID: PMC8552052 DOI: 10.1001/jamanetworkopen.2021.31503] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
This cross-sectional study compares randomized clinical trial protocols to assess the prevalence and reporting quality of planned subgroup analyses over time.
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Affiliation(s)
- Ala Taji Heravi
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Dmitry Gryaznov
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Stefan Schandelmaier
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Benjamin Kasenda
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Medical Oncology, University Hospital Basel, Basel, Switzerland
- Research and Development, iOMEDICO, Freiburg, Germany
| | - Matthias Briel
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
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14
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Gao Y, Liu M, Shi S, Niu M, Li J, Zhang J, Song F, Tian J. Prespecification of subgroup analyses and examination of treatment-subgroup interactions in cancer individual participant data meta-analyses are suboptimal. J Clin Epidemiol 2021; 138:156-167. [PMID: 34186194 DOI: 10.1016/j.jclinepi.2021.06.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 06/18/2021] [Accepted: 06/22/2021] [Indexed: 12/16/2022]
Abstract
OBJECTIVES This study aimed to explore the prespecification and conduct of subgroup analyses in cancer individual participant data meta-analyses (IPDMAs). STUDY DESIGN AND SETTING We searched PubMed, Embase.com, Cochrane Library, and Web of Science to identify IPDMAs of randomized controlled trials evaluating intervention effects for cancer. We evaluated how often cancer IPDMAs prespecify subgroup analyses and statistical approaches for examining treatment-subgroup interactions and handling continuous subgroup variables. RESULTS We included 89 IPDMAs, of which 41 (46.1%) reported a statistically significant treatment-subgroup interaction (P < 0.05) in at least one subgroup analysis. 47 (52.8%) IPDMAs prespecified methods for conducting subgroup analyses and the remaining 42 (47.2%) did not prespecify subgroup analyses. Of the 47 IPDMAs prespecified subgroup analyses, 19 performed the planned subgroup analyses, 21 added subgroup analyses, 7 reduced subgroup analyses. Eighty IPDMAs examined treatment-subgroup interactions, but 72 IPDMAs did not provide enough information to determine whether an appropriate approach that avoided aggregation bias was used. 85 IPDMAs that used continuous variables in subgroup analyses categorized continuous variables and only 1 IPDMA examined non-linear relationships. CONCLUSION Many cancer IPDMAs did not prespecify subgroup analyses, nor did they fully perform planned subgroup analyses. Lack of details for the test of treatment-subgroup interactions and examination of non-linear interactions was suboptimal.
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Affiliation(s)
- Ya Gao
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Ming Liu
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Shuzhen Shi
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Mingming Niu
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Jiang Li
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking UnionMedical College, Beijing, China
| | - Junhua Zhang
- Evidence-Based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Fujian Song
- Public Health and Health Services Research, Norwich Medical School, University of East Anglia, Norwich, UK.
| | - Jinhui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China; Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China.
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15
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Schandelmaier S, Briel M, Varadhan R, Schmid CH, Devasenapathy N, Hayward RA, Gagnier J, Borenstein M, van der Heijden GJMG, Dahabreh IJ, Sun X, Sauerbrei W, Walsh M, Ioannidis JPA, Thabane L, Guyatt GH. Development of the Instrument to assess the Credibility of Effect Modification Analyses (ICEMAN) in randomized controlled trials and meta-analyses. CMAJ 2021; 192:E901-E906. [PMID: 32778601 DOI: 10.1503/cmaj.200077] [Citation(s) in RCA: 286] [Impact Index Per Article: 95.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/06/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Most randomized controlled trials (RCTs) and meta-analyses of RCTs examine effect modification (also called a subgroup effect or interaction), in which the effect of an intervention varies by another variable (e.g., age or disease severity). Assessing the credibility of an apparent effect modification presents challenges; therefore, we developed the Instrument for assessing the Credibility of Effect Modification Analyses (ICEMAN). METHODS To develop ICEMAN, we established a detailed concept; identified candidate credibility considerations in a systematic survey of the literature; together with experts, performed a consensus study to identify key considerations and develop them into instrument items; and refined the instrument based on feedback from trial investigators, systematic review authors and journal editors, who applied drafts of ICEMAN to published claims of effect modification. RESULTS The final instrument consists of a set of preliminary considerations, core questions (5 for RCTs, 8 for meta-analyses) with 4 response options, 1 optional item for additional considerations and a rating of credibility on a visual analogue scale ranging from very low to high. An accompanying manual provides rationales, detailed instructions and examples from the literature. Seventeen potential users tested ICEMAN; their suggestions improved the user-friendliness of the instrument. INTERPRETATION The Instrument for assessing the Credibility of Effect Modification Analyses offers explicit guidance for investigators, systematic reviewers, journal editors and others considering making a claim of effect modification or interpreting a claim made by others.
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Affiliation(s)
- Stefan Schandelmaier
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont.
| | - Matthias Briel
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont
| | - Ravi Varadhan
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont
| | - Christopher H Schmid
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont
| | - Niveditha Devasenapathy
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont
| | - Rodney A Hayward
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont
| | - Joel Gagnier
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont
| | - Michael Borenstein
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont
| | - Geert J M G van der Heijden
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont
| | - Issa J Dahabreh
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont
| | - Xin Sun
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont
| | - Willi Sauerbrei
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont
| | - Michael Walsh
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont
| | - John P A Ioannidis
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont
| | - Lehana Thabane
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont
| | - Gordon H Guyatt
- Departments of Health Research Methods, Evidence, and Impact (Schandelmaier, Briel, Walsh, Thabane, Guyatt), Medicine (Walsh, Guyatt), Pediatrics (Thabane) and Anesthesia (Thabane), McMaster University, Hamilton, Ont.; Institute for Clinical Epidemiology and Biostatistics (Schandelmaier, Briel), Department of Clinical Research, Basel University, Basel, Switzerland; Division of Biostatistics and Bioinformatics (Varadhan), Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Md.; Department of Biostatistics (Schmid), Brown University School of Public Health, Brown University, Providence, RI; Indian institute of Public Health-Delhi (Devasenapathy), Public Health Foundation of India, New Delhi, India; VA Center for Clinical Management and Research (Hayward); Department of Internal Medicine (Hayward), University of Michigan School of Medicine; Department of Orthopaedic Surgery (Gagnier), University of Michigan; Department of Epidemiology (Gagnier), School of Public Health, University of Michigan, Ann Arbor, Mich.; Biostat (Borenstein), Englewood, NJ; Department of Social Dentistry (van der Heijden), Academic Center for Dentistry Amsterdam, University of Amsterdam and VU University Amsterdam, Amsterdam, Netherlands; Center for Evidence Synthesis in Health (Dahabreh) and Departments of Health Services, Policy, and Practice (Dahabreh) and Epidemiology (Dahabreh), School of Public Health, Brown University, Providence, RI; Chinese Evidence-Based Medicine Center (Sun), West China Hospital, Sichuan University, Chengdu, China; Institute of Medical Biometry and Statistics (Sauerbrei), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; Population Health Research Institute (Walsh), Hamilton Health Sciences/McMaster University, Hamilton, Ont.; Departments of Medicine (Ioannidis), Health Research and Policy (Ioannidis) and Biomedical Data Science (Ioannidis), and Statistics and Meta-Research Innovation Center at Stanford (METRICS) (Ioannidis), Stanford University, Stanford, Calif.; Biostatistics Unit (Thabane), St. Joseph's Healthcare, Hamilton, Ont
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16
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Thirard R, Ascione R, Blazeby JM, Rogers CA. Integrating expert opinions with clinical trial data to analyse low-powered subgroup analyses: a Bayesian analysis of the VeRDiCT trial. BMC Med Res Methodol 2020; 20:300. [PMID: 33302878 PMCID: PMC7727208 DOI: 10.1186/s12874-020-01178-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 11/25/2020] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Typically, subgroup analyses in clinical trials are conducted by comparing the intervention effect in each subgroup by means of an interaction test. However, trials are rarely, if ever, adequately powered for interaction tests, so clinically important interactions may go undetected. We discuss the application of Bayesian methods by using expert opinions alongside the trial data. We applied this methodology to the VeRDiCT trial investigating the effect of preoperative volume replacement therapy (VRT) versus no VRT (usual care) in diabetic patients undergoing cardiac surgery. Two subgroup effects were of clinical interest, a) preoperative renal failure and b) preoperative type of antidiabetic medication. METHODS Clinical experts were identified within the VeRDiCT trial centre in the UK. A questionnaire was designed to elicit opinions on the impact of VRT on the primary outcome of time from surgery until medically fit for hospital discharge, in the different subgroups. Prior beliefs of the subgroup effect of VRT were elicited face-to-face using two unconditional and one conditional questions per subgroup analysis. The robustness of results to the 'community of priors' was assessed. The community of priors was built using the expert priors for the mean average treatment effect, the interaction effect or both in a Bayesian Cox proportional hazards model implemented in the STAN software in R. RESULTS Expert opinions were obtained from 7 clinicians (6 cardiac surgeons and 1 cardiac anaesthetist). Participating experts believed VRT could reduce the length of recovery compared to usual care and the greatest benefit was expected in the subgroups with the more severe comorbidity. The Bayesian posterior estimates were more precise compared to the frequentist maximum likelihood estimate and were shifted toward the overall mean treatment effect. CONCLUSIONS In the VeRDiCT trial, the Bayesian analysis did not provide evidence of a difference in treatment effect across subgroups. However, this approach increased the precision of the estimated subgroup effects and produced more stable treatment effect point estimates than the frequentist approach. Trial methodologists are encouraged to prospectively consider Bayesian subgroup analyses when low-powered interaction tests are planned. TRIAL REGISTRATION ISRCTN, ISRCTN02159606 . Registered 29th October 2008.
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Affiliation(s)
- Russell Thirard
- Bristol Trials Centre (BTC), University of Bristol, Zone A, level 7, Bristol Royal Infirmary, Bristol, BS2 8HW, UK.
| | - Raimondo Ascione
- Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jane M Blazeby
- National Institute for Health Research Bristol Biomedical Research Centre, Bristol, UK
- Division of Surgery, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Chris A Rogers
- Bristol Trials Centre (BTC), University of Bristol, Zone A, level 7, Bristol Royal Infirmary, Bristol, BS2 8HW, UK
- National Institute for Health Research Bristol Biomedical Research Centre, Bristol, UK
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17
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Khan MS, Khan MAA, Irfan S, Siddiqi TJ, Greene SJ, Anker SD, Sreenivasan J, Friede T, Tahhan AS, Vaduganathan M, Fonarow GC, Butler J. Reporting and interpretation of subgroup analyses in heart failure randomized controlled trials. ESC Heart Fail 2020; 8:26-36. [PMID: 33254286 PMCID: PMC7835611 DOI: 10.1002/ehf2.13122] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 10/18/2020] [Accepted: 11/03/2020] [Indexed: 11/11/2022] Open
Abstract
Aims This study aimed to investigate the reporting of subgroup analyses in heart failure (HF) randomized controlled trials (RCTs) and to determine the strength and credibility of subgroup claims. Methods and results All primary HF RCTs published in nine high‐impact journals from 1 January 2008 to 31 December 2017 were included. Multivariable regression analysis was used to identify factors that may favour the reporting of results in specific subgroups. Strength of the subgroup effect claimed was classified into (i) strong, (ii) likely, or (iii) suggestive. Credibility of subgroup claim was scored using a pre‐specified 10 pointer criteria. Of the 261 HF RCTs studied, 107 (41%) reported subgroup analyses. Twenty‐five (23%) RCTs claimed a subgroup effect for the primary outcome of which six (24%) made a strong claim, eight (32%) claimed a likely effect, and 11 (44%) suggested a possible subgroup effect. Seven of the 25 RCTs did not employ interaction testing for subgroup claims of the primary outcome. Three out of 10 pre‐specified credibility criteria were satisfied by half of the trials. Fourteen trials justified the choice of subgroups, and 10 explicitly stated they were underpowered to detect differences within subgroups. Source of funding did not influence the frequency of reporting subgroup analyses (OR 0.53, 95% CI 0.78–3.62, P = 0.52). Conclusions Appropriate credibility criteria were rarely met even by HF RCTs that held strong subgroup claims. Subgroup analyses should be pre‐specified, be adequately powered, present interaction terms, and be replicated in independent data before being integrated into clinical decision making.
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Affiliation(s)
| | | | - Simra Irfan
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Tariq Jamal Siddiqi
- Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan
| | - Stephen J Greene
- Division of Cardiology, Duke University Medical Center, Durham, NC, USA
| | - Stefan D Anker
- Department of Cardiology (CVK) and Berlin Institute of Health Center for Regenerative Therapies (BCRT), German Centre for Cardiovascular Research (DZHK) partner site Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Jayakumar Sreenivasan
- Department of Cardiology, Westchester Medical Center and New York Medical College, Valhalla, NY, USA
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Goettingen and DZHK, partnerside Goettingen, Goettingen, Germany
| | - Ayman Samman Tahhan
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | | | - Gregg C Fonarow
- Division of Cardiology, Ronald Reagan-UCLA Medical Center, Los Angeles, CA, USA
| | - Javed Butler
- Department of Medicine, University of Mississippi, Jackson, MS, USA
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18
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Gryaznov D, Odutayo A, von Niederhäusern B, Speich B, Kasenda B, Ojeda-Ruiz E, Blümle A, Schandelmaier S, Mertz D, Tomonaga Y, Amstutz A, Pauli-Magnus C, Gloy V, Bischoff K, Wollmann K, Rehner L, Lohner S, Meerpohl JJ, Nordmann A, Klatte K, Ghosh N, Heravi AT, Wong J, Chow N, Hong PJ, Cord KM, Sricharoenchai S, Busse JW, Agarwal A, Saccilotto R, Schwenkglenks M, Moffa G, Hemkens LG, Hopewell S, von Elm E, Briel M. Rationale and design of repeated cross-sectional studies to evaluate the reporting quality of trial protocols: the Adherence to SPIrit REcommendations (ASPIRE) study and associated projects. Trials 2020; 21:896. [PMID: 33115541 PMCID: PMC7594472 DOI: 10.1186/s13063-020-04808-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 10/16/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Clearly structured and comprehensive protocols are an essential component to ensure safety of participants, data validity, successful conduct, and credibility of results of randomized clinical trials (RCTs). Funding agencies, research ethics committees (RECs), regulatory agencies, medical journals, systematic reviewers, and other stakeholders rely on protocols to appraise the conduct and reporting of RCTs. In response to evidence of poor protocol quality, the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guideline was published in 2013 to improve the accuracy and completeness of clinical trial protocols. The impact of these recommendations on protocol completeness and associations between protocol completeness and successful RCT conduct and publication remain uncertain. OBJECTIVES AND METHODS Aims of the Adherence to SPIrit REcommendations (ASPIRE) study are to investigate adherence to SPIRIT checklist items of RCT protocols approved by RECs in the UK, Switzerland, Germany, and Canada before (2012) and after (2016) the publication of the SPIRIT guidelines; determine protocol features associated with non-adherence to SPIRIT checklist items; and assess potential differences in adherence across countries. We assembled an international cohort of RCTs based on 450 protocols approved in 2012 and 402 protocols approved in 2016 by RECs in Switzerland, the UK, Germany, and Canada. We will extract data on RCT characteristics and adherence to SPIRIT for all included protocols. We will use multivariable regression models to investigate temporal changes in SPIRIT adherence, differences across countries, and associations between SPIRIT adherence of protocols with RCT registration, completion, and publication of results. We plan substudies to examine the registration, premature discontinuation, and non-publication of RCTs; the use of patient-reported outcomes in RCT protocols; SPIRIT adherence of RCT protocols with non-regulated interventions; the planning of RCT subgroup analyses; and the use of routinely collected data for RCTs. DISCUSSION The ASPIRE study and associated substudies will provide important information on the impact of measures to improve the reporting of RCT protocols and on multiple aspects of RCT design, trial registration, premature discontinuation, and non-publication of RCTs observing potential changes over time.
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Affiliation(s)
- Dmitry Gryaznov
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Spitalstrasse 12, 4031 Basel, Switzerland
| | - Ayodele Odutayo
- Applied Health Research Centre, Li Ka Shing Knowledge Instiute of St Michael’s Hospital, Toronto, Canada
- Oxford Clinical Trials Research Unit and Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Belinda von Niederhäusern
- Department of Clinical Research, Clinical Trial Unit, University Hospital Basel and University of Basel, Basel, Switzerland
- Roche Pharma AG, Grenzach-Wyhlen, Germany
| | - Benjamin Speich
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Spitalstrasse 12, 4031 Basel, Switzerland
- Oxford Clinical Trials Research Unit and Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Benjamin Kasenda
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Spitalstrasse 12, 4031 Basel, Switzerland
- Department of Medical Oncology, University Hospital Basel, Basel, Switzerland
- iOMEDICO AG, Research & Development, Freiburg, Germany
| | - Elena Ojeda-Ruiz
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Spitalstrasse 12, 4031 Basel, Switzerland
- Preventive Medicine Department, Osakidetza Basque Health Service, Bioaraba Health Research Institute, Health Prevention, Promotion and Care Area, Araba University Hospital, Vitoria-Gasteiz, Spain
| | - Anette Blümle
- Institute for Evidence in Medicine, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Cochrane Germany, Cochrane Germany Foundation, Freiburg, Germany
| | - Stefan Schandelmaier
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Spitalstrasse 12, 4031 Basel, Switzerland
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Dominik Mertz
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Yuki Tomonaga
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Alain Amstutz
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Spitalstrasse 12, 4031 Basel, Switzerland
- Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, Basel, Switzerland
| | - Christiane Pauli-Magnus
- Department of Clinical Research, Clinical Trial Unit, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Viktoria Gloy
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Spitalstrasse 12, 4031 Basel, Switzerland
| | - Karin Bischoff
- Institute for Evidence in Medicine, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Cochrane Germany, Cochrane Germany Foundation, Freiburg, Germany
| | - Katharina Wollmann
- Institute for Evidence in Medicine, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Cochrane Germany, Cochrane Germany Foundation, Freiburg, Germany
| | - Laura Rehner
- Institute for Evidence in Medicine, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Epidemiology and Community Health, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Szimonetta Lohner
- Cochrane Hungary, Clinical Centre of the University of Pécs, Medical School, University of Pécs, Pécs, Hungary
| | - Joerg J. Meerpohl
- Institute for Evidence in Medicine, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Cochrane Germany, Cochrane Germany Foundation, Freiburg, Germany
| | - Alain Nordmann
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Spitalstrasse 12, 4031 Basel, Switzerland
| | - Katharina Klatte
- Department of Clinical Research, Clinical Trial Unit, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Nilabh Ghosh
- Department of Neurosurgery and Department of Biomedicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Ala Taji Heravi
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Spitalstrasse 12, 4031 Basel, Switzerland
| | - Jacqueline Wong
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Ngai Chow
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Patrick Jiho Hong
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
- Department of Anesthesiology and Pain Medicine, University of Toronto, Toronto, Canada
| | - Kimberly Mc Cord
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Spitalstrasse 12, 4031 Basel, Switzerland
| | - Sirintip Sricharoenchai
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Spitalstrasse 12, 4031 Basel, Switzerland
| | - Jason W. Busse
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
- Department of Anesthesia, McMaster University, Hamilton, Canada
| | - Arnav Agarwal
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Ramon Saccilotto
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Spitalstrasse 12, 4031 Basel, Switzerland
| | - Matthias Schwenkglenks
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
- Institute of Pharmaceutical Medicine (ECPM), University of Basel, Basel, Switzerland
| | - Giusi Moffa
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Spitalstrasse 12, 4031 Basel, Switzerland
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Lars G. Hemkens
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Spitalstrasse 12, 4031 Basel, Switzerland
| | - Sally Hopewell
- Oxford Clinical Trials Research Unit and Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Erik von Elm
- Cochrane Switzerland, Centre for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
| | - Matthias Briel
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Spitalstrasse 12, 4031 Basel, Switzerland
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
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19
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Dias J, Brealey S, Cook L, Fairhurst C, Hinde S, Leighton P, Choudhary S, Costa M, Hewitt C, Hodgson S, Jefferson L, Jeyapalan K, Keding A, Northgraves M, Palmer J, Rangan A, Richardson G, Taub N, Tew G, Thompson J, Torgerson D. Surgical fixation compared with cast immobilisation for adults with a bicortical fracture of the scaphoid waist: the SWIFFT RCT. Health Technol Assess 2020; 24:1-234. [PMID: 33109331 PMCID: PMC7681317 DOI: 10.3310/hta24520] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Scaphoid fractures account for 90% of carpal fractures and occur predominantly in young men. Immediate surgical fixation of this fracture has increased. OBJECTIVE To compare the clinical effectiveness and cost-effectiveness of surgical fixation with cast treatment and early fixation in adults with scaphoid waist fractures that fail to unite. DESIGN Multicentre, pragmatic, open-label, parallel two-arm randomised controlled trial with an economic evaluation and a nested qualitative study. SETTING Orthopaedic departments of 31 hospitals in England and Wales recruited from July 2013, with final follow-up in September 2017. PARTICIPANTS Adults (aged ≥ 16 years) presenting within 2 weeks of injury with a clear, bicortical fracture of the scaphoid waist on plain radiographs. INTERVENTIONS Early surgical fixation using Conformité Européenne-marked headless compression screws. Below-elbow cast immobilisation for 6-10 weeks and urgent fixation of confirmed non-union. MAIN OUTCOME MEASURES The primary outcome and end point was the Patient-Rated Wrist Evaluation total score at 52 weeks, with a clinically relevant difference of 6 points. Secondary outcomes included Patient-Rated Wrist Evaluation pain and function subscales, Short Form questionnaire 12-items, bone union, range of movement, grip strength, complications and return to work. RESULTS The mean age of 439 participants was 33 years; 363 participants were male (83%) and 269 participants had an undisplaced fracture (61%). The primary analysis was on 408 participants with valid Patient-Rated Wrist Evaluation outcome data for at least one post-randomisation time point (surgery, n = 203 of 219; cast, n = 205 of 220). There was no clinically relevant difference in the Patient-Rated Wrist Evaluation total score at 52 weeks: the mean score in the cast group was 14.0 (95% confidence interval 11.3 to 16.6) and in the surgery group was 11.9 (95% confidence interval 9.2 to 14.5), with an adjusted mean difference of -2.1 in favour of surgery (95% confidence interval -5.8 to 1.6; p = 0.27). The non-union rate was low (surgery group, n = 1; cast group, n = 4). Eight participants in the surgery group had a total of 11 reoperations and one participant in the cast group required a reoperation for non-union. The base-case economic analysis at 52 weeks found that surgery cost £1295 per patient more (95% confidence interval £1084 to £1504) than cast treatment. The base-case analysis of a lifetime-extrapolated model confirmed that the cast treatment pathway was more cost-effective. The nested qualitative study identified patients' desire to have a 'sense of recovering', which surgeons should address at the outset. LIMITATION There were 17 participants who had initial cast treatment and surgery for confirmed non-union, which in 14 cases was within 6 months from randomisation and in three cases was after 6 months. Three of the four participants in the cast group who had a non-union at 52 weeks were not offered surgery. CONCLUSIONS Adult patients with an undisplaced or minimally displaced scaphoid waist fracture should have cast immobilisation and suspected non-unions immediately confirmed and urgently fixed. Patients should be followed up at 5 years to investigate the effect of partial union, degenerative arthritis, malunion and screw problems on their quality of life. TRIAL REGISTRATION Current Controlled Trials ISRCTN67901257. FUNDING This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 52. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Joseph Dias
- University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Stephen Brealey
- Alcuin Research Resource Centre Building, Department of Health Sciences, University of York, York, UK
| | - Liz Cook
- Alcuin Research Resource Centre Building, Department of Health Sciences, University of York, York, UK
| | - Caroline Fairhurst
- Alcuin Research Resource Centre Building, Department of Health Sciences, University of York, York, UK
| | | | - Paul Leighton
- School of Medicine, University of Nottingham, Queen's Medical Centre, Nottingham, UK
| | - Surabhi Choudhary
- Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Matthew Costa
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Kadoorie Centre, John Radcliffe Hospital, Oxford, UK
| | - Catherine Hewitt
- Alcuin Research Resource Centre Building, Department of Health Sciences, University of York, York, UK
| | - Stephen Hodgson
- Department of Orthopaedic Surgery, Bolton NHS Foundation Trust, Royal Bolton Hospital, Bolton, UK
| | - Laura Jefferson
- Alcuin Research Resource Centre Building, Department of Health Sciences, University of York, York, UK
- Department of Health Sciences, University of York, York, UK
| | | | - Ada Keding
- Alcuin Research Resource Centre Building, Department of Health Sciences, University of York, York, UK
| | - Matthew Northgraves
- Alcuin Research Resource Centre Building, Department of Health Sciences, University of York, York, UK
| | - Jared Palmer
- University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Amar Rangan
- Alcuin Research Resource Centre Building, Department of Health Sciences, University of York, York, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | | | - Nicholas Taub
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Garry Tew
- Alcuin Research Resource Centre Building, Department of Health Sciences, University of York, York, UK
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, UK
| | - John Thompson
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - David Torgerson
- Alcuin Research Resource Centre Building, Department of Health Sciences, University of York, York, UK
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20
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Promoting Learning from Null or Negative Results in Prevention Science Trials. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2020; 23:751-763. [PMID: 32748164 PMCID: PMC7398716 DOI: 10.1007/s11121-020-01140-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
There can be a tendency for investigators to disregard or explain away null or negative results in prevention science trials. Examples include not publicizing findings, conducting spurious subgroup analyses, or attributing the outcome post hoc to real or perceived weaknesses in trial design or intervention implementation. This is unhelpful for several reasons, not least that it skews the evidence base, contributes to research “waste”, undermines respect for science, and stifles creativity in intervention development. In this paper, we identify possible policy and practice responses when interventions have null (ineffective) or negative (harmful) results, and argue that these are influenced by: the intervention itself (e.g., stage of gestation, perceived importance); trial design, conduct, and results (e.g., pattern of null/negative effects, internal and external validity); context (e.g., wider evidence base, state of policy); and individual perspectives and interests (e.g., stake in the intervention). We advance several strategies to promote more informative null or negative effect trials and enable learning from such results, focusing on changes to culture, process, intervention design, trial design, and environment.
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21
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McCarthy OL, Aliaga C, Torrico Palacios ME, López Gallardo J, Huaynoca S, Leurent B, Edwards P, Palmer M, Ahamed I, Free C. An Intervention Delivered by Mobile Phone Instant Messaging to Increase Acceptability and Use of Effective Contraception Among Young Women in Bolivia: Randomized Controlled Trial. J Med Internet Res 2020; 22:e14073. [PMID: 32568092 PMCID: PMC7338928 DOI: 10.2196/14073] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 11/07/2019] [Accepted: 01/24/2020] [Indexed: 12/04/2022] Open
Abstract
Background Although the most effective methods of contraception are available in Bolivia, unmet need for contraception among women aged 15 to 19 years is estimated to be 38% (2008), and the adolescent fertility rate is 71 per 1000 women (2016). Mobile phones are a popular mode to deliver health behavior support. We developed a contraceptive behavioral intervention for young Bolivian women delivered by mobile phone and guided by behavioral science. The intervention consists of short instant messages sent through an app over 4 months. Objective This trial aimed to evaluate the effect of the intervention on young Bolivian women’s use of and attitudes toward the effective contraceptive methods available in Bolivia. Methods This was a parallel group, individually randomized superiority trial with a 1:1 allocation ratio. Women were eligible if they were aged 16 to 24 years, owned a personal Android mobile phone, lived in La Paz or El Alto, reported an unmet need for contraception, and could read Spanish. The target sample size was 1310 participants. Participants allocated to the intervention had access to an app with standard family planning information and intervention messages. Participants allocated to the control group had access to the same app and control messages. Coprimary outcomes were use of effective contraception and acceptability of at least one method of effective contraception at 4 months. Secondary outcomes were use of effective contraception during the study, acceptability of the individual methods, service uptake, unintended pregnancy, and abortion. Process outcomes included knowledge, perceived norms, personal agency, and intention. Outcomes were analyzed using logistic and linear regression. We also asked participants about physical violence. Results A total of 640 participants were enrolled, and 67.0% (429) of them contributed follow-up data for the coprimary outcome, the use of effective contraception. There was no evidence that use differed between the groups (33% control vs 37% intervention; adjusted odds ratio [OR] 1.19, 95% CI 0.80 to 1.77; P=.40). There was a borderline significant effect regarding acceptability (63% control vs 72% intervention; adjusted OR 1.49, 95% CI 0.98 to 2.28; P=.06). There were no statistically significant differences in any of the secondary or process outcomes. The intervention dose received was low. In the control group, 2.8% (6/207) reported experiencing physical violence compared with 1.9% (4/202) in the intervention group (Fisher exact test P=.75). Conclusions This trial was unable to provide definitive conclusions regarding the effect of the intervention on use and acceptability of effective contraception because of under recruitment. Although we cannot strongly recommend implementation, the results suggest that it would be safe and may increase the acceptability of effective contraception if the intervention messages were offered alongside the download of the app. Trial Registration ClinicalTrials.gov NCT02905526; https://clinicaltrials.gov/ct2/show/NCT02905526
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Affiliation(s)
- Ona L McCarthy
- The London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | | | | | - Silvia Huaynoca
- International Planned Parenthood Federation/Western Hemisphere Region, New York, NY, United States
| | - Baptiste Leurent
- The London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Phil Edwards
- The London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Melissa Palmer
- The London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Irrfan Ahamed
- The London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Caroline Free
- The London School of Hygiene and Tropical Medicine, London, United Kingdom
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22
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Bentley C, Sundquist S, Dancey J, Peacock S. Barriers to conducting cancer trials in Canada: an analysis of key informant interviews. ACTA ACUST UNITED AC 2020; 27:e307-e312. [PMID: 32669937 DOI: 10.3747/co.27.5707] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background In Canada, there is growing evidence that oncology clinical trials units (ctus) and programs face serious financial challenges. Investment in cancer research in Canada has declined almost 20% in the 5 years since its peak in 2011, and the costs of conducting leading-edge trials are rising. Clinical trials units must therefore be strategic about which studies they open. We interviewed Canadian health care professionals responsible for running cancer trials programs to identify the barriers to sustainability that they face. Methods One-on-one telephone interviews were conducted with clinicians and clinical research professionals at oncology ctus in Canada. We asked for their perspectives about the barriers to conducting trials at their institutions, in their provinces, and nationwide. Interviews were digitally recorded, transcribed, anonymized, and coded in the NVivo software application (version 11: QSR International, Melbourne, Australia). The initial coding structure was informed by the interview script, with new concepts drawn out and coded during analysis, using a constant comparative approach. Results Between June 2017 and November 2018, 25 interviews were conducted. Key barriers that participants identified were■ insufficient stable funding to support trials infrastructure and retain staff;■ the need to adopt strict cost-recovery policies, leading to fewer academic trials in portfolios; and■ an overreliance on industry to fund clinical research in Canada. Conclusions Funding uncertainties have led ctus to increasingly rely on industry sponsorship and more stringent feasibility thresholds to remain solvent. Retaining skilled trials staff can create efficiencies in opening and running studies, with spillover effects of more trials being open to patients. More academic studies are needed to curb industry's influence.
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Affiliation(s)
- C Bentley
- Canadian Centre for Applied Research in Cancer Control, Vancouver, BC.,Department of Cancer Control Research, BC Cancer, Vancouver, BC
| | - S Sundquist
- Canadian Cancer Clinical Trials Network, Toronto, ON.,Ontario Institute for Cancer Research, Toronto, ON
| | - J Dancey
- Canadian Cancer Clinical Trials Network, Toronto, ON.,Ontario Institute for Cancer Research, Toronto, ON.,Department of Oncology, School of Medicine, Queens University, Kingston, ON.,Canadian Cancer Trials Group, Kingston, ON
| | - S Peacock
- Canadian Centre for Applied Research in Cancer Control, Vancouver, BC.,Department of Cancer Control Research, BC Cancer, Vancouver, BC.,Faculty of Health Sciences, Simon Fraser University, Burnaby, BC
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23
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Schandelmaier S, Schmitt AM, Herbrand AK, Glinz D, Ewald H, Briel M, Guyatt GH, Hemkens LG, Kasenda B. Characteristics and interpretation of subgroup analyses based on tumour characteristics in randomised trials testing target-specific anticancer drugs: design of a systematic survey. BMJ Open 2020; 10:e034565. [PMID: 32474426 PMCID: PMC7264639 DOI: 10.1136/bmjopen-2019-034565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 03/02/2020] [Accepted: 04/22/2020] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Target-specific anticancer drugs are under rapid development. Little is known, however, about the risk of administering target-specific drugs to patients who have tumours with molecular alterations or other characteristics that can make the drug ineffective or even harmful. An increasing number of randomised clinical trials (RCTs) investigating target-specific anticancer drugs include subgroup analyses based on tumour characteristics. Such subgroup analyses have the potential to be more credible and influential than subgroup analyses based on traditional factors such as sex or tumour stage. In addition, they may more frequently lead to qualitative subgroup effects, that is, show benefit in one but harm in another subgroup of patients (eg, if the tumour characteristic makes the drug ineffective or even enhance tumour growth). If so, subgroup analyses based on tumour characteristics would be highly relevant for patient safety. The aim of this study is to systematically assess the frequency and characteristics of subgroup analyses based on tumour characteristics, the frequency of qualitative subgroup effects, their credibility, and the interpretations that investigators and guidelines developers report. METHODS AND ANALYSIS We will perform a systematic survey of 433 RCTs testing the effect of target-specific anticancer drugs. Teams of methodologically trained investigators and oncologists will identify eligible studies, extract relevant data and assess the credibility of putative subgroup effects using a recently developed formal instrument. We will systematically assess how trial investigators interpret apparent subgroup effects based on tumour characteristics and the extent to which they influence subsequent practice guidelines. Our results will provide empirical data characterising an increasingly used type of subgroup analysis in cancer trials and its potential impact on precision medicine to predict benefit or harm. ETHICS AND DISSEMINATION Formal ethical approval is not required for this study. We will disseminate the findings in a peer-reviewed and open-access journal publication.
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Affiliation(s)
- Stefan Schandelmaier
- Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital and University of Basel, Basel, Switzerland
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Andreas M Schmitt
- Department of Medical Oncology, University Hospital Basel, Basel, Switzerland
| | - Amanda K Herbrand
- Department of Medical Oncology, University Hospital Basel, Basel, Switzerland
| | - Dominik Glinz
- Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital and University of Basel, Basel, Switzerland
| | - Hannah Ewald
- University Medical Library, University of Basel, Basel, Switzerland
| | - Matthias Briel
- Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital and University of Basel, Basel, Switzerland
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Gordon H Guyatt
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Lars G Hemkens
- Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital and University of Basel, Basel, Switzerland
| | - Benjamin Kasenda
- Department of Medical Oncology, University Hospital Basel, Basel, Switzerland
- Research and Development, iOMEDICO AG, Freiburg, Germany
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24
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Milojevic M, Nikolic A, Jüni P, Head SJ. A statistical primer on subgroup analyses. Interact Cardiovasc Thorac Surg 2020; 30:839-845. [DOI: 10.1093/icvts/ivaa042] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 01/23/2020] [Accepted: 01/29/2020] [Indexed: 11/12/2022] Open
Abstract
Abstract
Resources for clinical research are limited. With increasing demand for patient-centred care, which is growing into an integral component of modern medicine, studying outcomes of patients with specific clinical characteristics is becoming increasingly important. Given the high cost of clinical trials and the time it takes to complete an investigation, it has become compulsory for investigators to assess not only treatment effects between the main randomized groups but also to try to identify clinically relevant subgroups that may particularly benefit from specific treatments. Publications of subgroup analyses turned out to be prevalent, and more importantly, these findings play a significant role in strategic planning and decision-making processes. Therefore, raising awareness among clinicians about the concepts and values of subgroup analysis is an aspect of improving patient outcomes. In this statistical primer, we give a broad introduction to the topic of subgroup analysis in scientific research. We furthermore discuss the concept of subgroup analysis; the motivation for assessing subgroups; the types of subgroup analyses and the paradigm of hypothesis-generating research; the proper statistical methods for the examination of subgroup effects; and the optimal approach for interpretation of results. Finally, this review establishes the comprehensive users’ guide for analysing and reporting subgroup studies on a point-by-point basis, using real-world examples that may help readers to gain experience to pursue their own subgroup analyses or interpret those of others.
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Affiliation(s)
- Milan Milojevic
- Department of Cardiothoracic Surgery, Erasmus University Medical Center, Rotterdam, Netherlands
- Department of Cardiac Surgery and Cardiovascular Research, Dedinje Cardiovascular Institute, Belgrade, Serbia
| | - Aleksandar Nikolic
- Department of Cardiac Surgery, Acibadem Sistina Hospital, Skopje, North Macedonia
| | - Peter Jüni
- Applied Health Research Centre, Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Stuart J Head
- Department of Cardiothoracic Surgery, Erasmus University Medical Center, Rotterdam, Netherlands
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25
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Nikolic M, Konic Ristic A, González-Sarrías A, Istas G, Urpi-Sarda M, Dall'Asta M, Monfoulet LE, Cloetens L, Bayram B, Tumolo MR, Chervenkov M, Scoditti E, Massaro M, Tejera N, Abadjieva D, Chambers K, Krga I, Tomás-Barberán FA, Morand C, Feliciano R, García-Villalba R, Garcia-Aloy M, Mena P. Improving the reporting quality of intervention trials addressing the inter-individual variability in response to the consumption of plant bioactives: quality index and recommendations. Eur J Nutr 2019; 58:49-64. [PMID: 31492976 PMCID: PMC6851030 DOI: 10.1007/s00394-019-02069-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 07/23/2019] [Indexed: 02/06/2023]
Abstract
PURPOSE The quality of the study design and data reporting in human trials dealing with the inter-individual variability in response to the consumption of plant bioactives is, in general, low. There is a lack of recommendations supporting the scientific community on this topic. This study aimed at developing a quality index to assist the assessment of the reporting quality of intervention trials addressing the inter-individual variability in response to plant bioactive consumption. Recommendations for better designing and reporting studies were discussed. METHODS The selection of the parameters used for the development of the quality index was carried out in agreement with the scientific community through a survey. Parameters were defined, grouped into categories, and scored for different quality levels. The applicability of the scoring system was tested in terms of consistency and effort, and its validity was assessed by comparison with a simultaneous evaluation by experts' criteria. RESULTS The "POSITIVe quality index" included 11 reporting criteria grouped into four categories (Statistics, Reporting, Data presentation, and Individual data availability). It was supported by detailed definitions and guidance for their scoring. The quality index score was tested, and the index demonstrated to be valid, reliable, and responsive. CONCLUSIONS The evaluation of the reporting quality of studies addressing inter-individual variability in response to plant bioactives highlighted the aspects requiring major improvements. Specific tools and recommendations favoring a complete and transparent reporting on inter-individual variability have been provided to support the scientific community on this field.
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Affiliation(s)
- Marina Nikolic
- Institute for Medical Research, University of Belgrade, Belgrade, Serbia
| | - Aleksandra Konic Ristic
- Institute for Medical Research, University of Belgrade, Belgrade, Serbia.
- UCD Institute of Food and Health, University College Dublin, Belfield, Dublin, Ireland.
| | - Antonio González-Sarrías
- Laboratory of Food and Health, Research Group on Quality, Safety and Bioactivity of Plant Foods, CEBAS-CSIC, Murcia, Spain
| | - Geoffrey Istas
- Department of Nutritional Sciences, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK
| | - Mireia Urpi-Sarda
- Biomarkers and Nutrimetabolomic Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, University of Barcelona, Santa Coloma De Gramenet, Spain
- CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - Margherita Dall'Asta
- Human Nutrition Unit, Department of Food and Drugs, University of Parma, Medical School Building C, Via Volturno, 39, 43125, Parma, Italy
| | - Laurent-Emmanuel Monfoulet
- Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA), Université Clermont Auvergne, CRNH Auvergne, Clermont-Ferrand, France
| | - Lieselotte Cloetens
- Biomedical Nutrition, Pure and Applied Biochemistry, Lund University, Lund, Sweden
| | - Banu Bayram
- Department of Nutrition and Dietetics, University of Health Sciences, Istanbul, Turkey
| | - Maria Rosaria Tumolo
- Research Unit of Brindisi, Institute for Research on Population and Social Policies, National Research Council, Brindisi, Italy
| | - Mihail Chervenkov
- Faculty of Veterinary Medicine, University of Forestry, Sofia, Bulgaria
- Institute of Neurobiology, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | - Egeria Scoditti
- Institute of Clinical Physiology (IFC), National Research Council (CNR), Lecce, Italy
| | - Marika Massaro
- Institute of Clinical Physiology (IFC), National Research Council (CNR), Lecce, Italy
| | - Noemi Tejera
- Department of Nutrition and Preventive Medicine, Norwich Medical School, University of East Anglia, Norwich, UK
| | - Desislava Abadjieva
- Institute of Biology and Immunology of Reproduction, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | - Karen Chambers
- Quadram Institute Bioscience, Norwich Research Park, Norwich, UK
| | - Irena Krga
- Institute for Medical Research, University of Belgrade, Belgrade, Serbia
| | - Francisco A Tomás-Barberán
- Laboratory of Food and Health, Research Group on Quality, Safety and Bioactivity of Plant Foods, CEBAS-CSIC, Murcia, Spain
| | - Christine Morand
- Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA), Université Clermont Auvergne, CRNH Auvergne, Clermont-Ferrand, France
| | - Rodrigo Feliciano
- Division of Cardiology, Pulmonology, and Vascular Medicine, Medical Faculty, University of Duesseldorf, Dusseldorf, Germany
| | - Rocío García-Villalba
- Laboratory of Food and Health, Research Group on Quality, Safety and Bioactivity of Plant Foods, CEBAS-CSIC, Murcia, Spain
| | - Mar Garcia-Aloy
- Biomarkers and Nutrimetabolomic Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, University of Barcelona, Santa Coloma De Gramenet, Spain
- CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, Spain
| | - Pedro Mena
- Human Nutrition Unit, Department of Food and Drugs, University of Parma, Medical School Building C, Via Volturno, 39, 43125, Parma, Italy.
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Caparrotta TM, Dear JW, Colhoun HM, Webb DJ. Pharmacoepidemiology: Using randomised control trials and observational studies in clinical decision-making. Br J Clin Pharmacol 2019; 85:1907-1924. [PMID: 31206748 DOI: 10.1111/bcp.14024] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 04/23/2019] [Accepted: 05/24/2019] [Indexed: 12/14/2022] Open
Abstract
Weighing up sources of evidence is a key skill for clinical decision-makers. Randomised controlled trials (RCTs) and observational studies each have advantages and disadvantages, and in both cases perceived weaknesses can be improved through modifications of design and analysis. In the field of pharmacoepidemiology, RCTs are the best way to determine whether an intervention modifies an outcome being studied, largely because randomisation reduces bias and confounding. Observational studies are useful to investigate whether benefits/harms of a treatment are seen in day-to-day clinical practice in a wider group of patients. Although observational studies, even in a small cohort, can provide very useful clinical evidence, they may also be misleading (as shown by subsequent RCTs), in part because of allocation bias. There is an unmet need for clinicians to become well versed in appraising the study design and statistical analysis of observational pharmacoepidemiology (OP) studies, rather like the medical training already offered for RCT evaluation. This is because OP studies are likely to become more common with the computerisation of healthcare records and increasingly contribute to the evidence base available for clinical decision-making. However, when the results of an RCT conflict with the results of an OP study, the findings of the RCT should be preferred, especially if its findings have been repeated elsewhere. Conversely, OP studies that align with the findings of RCTs can provide rich and useful information to complement that generated by RCTs.
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Affiliation(s)
| | - James W Dear
- Queen's Medical Research Institute, University of Edinburgh, UK
| | - Helen M Colhoun
- Institute of Genetics and Molecular Medicine, University of Edinburgh, UK
| | - David J Webb
- Queen's Medical Research Institute, University of Edinburgh, UK
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27
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Schandelmaier S, Chang Y, Devasenapathy N, Devji T, Kwong JSW, Colunga Lozano LE, Lee Y, Agarwal A, Bhatnagar N, Ewald H, Zhang Y, Sun X, Thabane L, Walsh M, Briel M, Guyatt GH. A systematic survey identified 36 criteria for assessing effect modification claims in randomized trials or meta-analyses. J Clin Epidemiol 2019; 113:159-167. [PMID: 31132471 DOI: 10.1016/j.jclinepi.2019.05.014] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 05/14/2019] [Accepted: 05/20/2019] [Indexed: 02/05/2023]
Abstract
OBJECTIVE The objective of the study was to systematically survey the methodological literature and collect suggested criteria for assessing the credibility of effect modification and associated rationales. STUDY DESIGN AND SETTING We searched MEDLINE, Embase, and WorldCat up to March 2018 for publications providing guidance for assessing the credibility of effect modification identified in randomized trials or meta-analyses. Teams of two investigators independently identified eligible publications and extracted credibility criteria and authors' rationale, reaching consensus through discussion. We created a taxonomy of criteria that we iteratively refined during data abstraction. RESULTS We identified 150 eligible publications that provided 36 criteria and associated rationales. Frequent criteria included significant test for interaction (n = 54), a priori hypothesis (n = 49), providing a causal explanation (n = 47), accounting for multiplicity (n = 45), testing a small number of effect modifiers (n = 38), and prespecification of analytic details (n = 39). For some criteria, we found more than one rationale; some criteria were connected through a common rationale. For some criteria, experts disagreed regarding their suitability (e.g., added value of stratified randomization; trustworthiness of biologic rationales). CONCLUSION Methodologists have expended substantial intellectual energy providing criteria for critical appraisal of apparent effect modification. Our survey highlights popular criteria, expert agreement and disagreement, and where more work is needed, including testing criteria in practice.
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Affiliation(s)
- Stefan Schandelmaier
- Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada; Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University of Basel and University Hospital Basel, Spitalstrasse 12, 4056 Basel, Switzerland.
| | - Yaping Chang
- Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada
| | - Niveditha Devasenapathy
- Indian Institute of Public Health-Delhi, Public Health Foundation of India, Plot 47, Sector 44, Institutional Area, Gurgaon, 122002 Haryana, India
| | - Tahira Devji
- Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada
| | - Joey S W Kwong
- JC School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
| | - Luis E Colunga Lozano
- Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada
| | - Yung Lee
- Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada; Michael G. DeGroote School of Medicine, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada
| | - Arnav Agarwal
- Department of Medicine, University of Toronto, 190 Elizabeth Street, R. Fraser Elliott Building, 3-805, Toronto, Ontario M5G 2C4, Canada
| | - Neera Bhatnagar
- Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada
| | - Hannah Ewald
- Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University of Basel and University Hospital Basel, Spitalstrasse 12, 4056 Basel, Switzerland
| | - Ying Zhang
- Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada; Center for Evidence-based Chinese Medicine, Beijing University of Chinese Medicine, 11 Bei San Huan Dong Lu, Chaoyang, Beijing 100029, China
| | - Xin Sun
- Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Lehana Thabane
- Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada; Biostatistics Unit, St Joseph's Healthcare - Hamilton, 50 Charlton Street East, Hamilton, Ontario L8N 4A6, Canada
| | - Michael Walsh
- Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada; Department of Medicine, McMaster University, 1200 Main Street West, Hamilton, Ontario L8S 4L8, Canada
| | - Matthias Briel
- Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada; Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University of Basel and University Hospital Basel, Spitalstrasse 12, 4056 Basel, Switzerland
| | - Gordon H Guyatt
- Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada; Department of Medicine, McMaster University, 1200 Main Street West, Hamilton, Ontario L8S 4L8, Canada
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28
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Fan J, Song F, Bachmann MO. Justification and reporting of subgroup analyses were lacking or inadequate in randomized controlled trials. J Clin Epidemiol 2019; 108:17-25. [DOI: 10.1016/j.jclinepi.2018.12.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 12/05/2018] [Accepted: 12/11/2018] [Indexed: 01/11/2023]
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Burke SL, Hu T, Spadola CE, Burgess A, Li T, Cadet T. Treatment of Sleep Disturbance May Reduce the Risk of Future Probable Alzheimer's Disease. J Aging Health 2019; 31:322-342. [PMID: 30160576 PMCID: PMC6328323 DOI: 10.1177/0898264318795567] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
OBJECTIVE This study explored two research questions: (a) Does sleep medication neutralize or provide a protective effect against the hazard of Alzheimer's disease (AD)? (b) Do apolipoprotein (APOE) e4 carriers reporting a sleep disturbance experience an increased risk of AD? METHOD This study is a secondary analysis of the National Alzheimer's Coordinating Center's Uniform Data Set ( n = 6,782) using Cox proportional hazards regression. RESULTS Sleep disturbance was significantly associated with eventual AD development. Among the subset of participants taking general sleep medications, no relationship between sleep disturbance and eventual AD was observed. Among individuals not taking sleep medications, the increased hazard between the two variables remained. Among APOE e4 carriers, sleep disturbance and AD were significant, except among those taking zolpidem. DISCUSSION Our findings support the emerging link between sleep disturbance and AD. Our findings also suggest a continued need to elucidate the mechanisms that offer protective factors against AD development.
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Affiliation(s)
| | - Tianyan Hu
- Florida International University, Miami, USA
| | | | | | - Tan Li
- Florida International University, Miami, USA
| | - Tamara Cadet
- Simmons College, Boston, MA, USA
- Harvard School of Dental Medicine, Boston, MA, USA
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Bentley C, Cressman S, van der Hoek K, Arts K, Dancey J, Peacock S. Conducting clinical trials-costs, impacts, and the value of clinical trials networks: A scoping review. Clin Trials 2019; 16:183-193. [PMID: 30628466 DOI: 10.1177/1740774518820060] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND A significant barrier to conducting clinical trials is their high cost, which is driven primarily by the time and resources required to activate trials and reach accrual targets. The high cost of running trials has a substantial impact on their long-term feasibility and the type of clinical research undertaken. METHODS A scoping review of the empirical literature on the costs associated with conducting clinical trials was undertaken for the years 2001-2015. Five reference databases were consulted to elicit how trials costs are presented in the literature. A review instrument was developed to extract the content of in-scope papers. Findings were characterized by date and place of publication, clinical disease area, and network/cooperative group designation, when specified. Costs were captured and grouped by patient accrual and management, infrastructure, and the opportunity costs associated with industry funding for trials research. Cost impacts on translational research and health systems were also captured, as were recommendations to reduce trial expenditures. Since articles often cited multiple costs, multiple cost coding was used during data extraction to capture the range and frequency of costs. RESULTS A total of 288 empirical articles were included. The distribution of reported costs was: patient management and accrual costs (132 articles), infrastructure costs (118 articles) and the opportunity costs of industry sponsorship (72 articles). 221 articles reported on the impact of undertaking costly trials on translational research and health systems; of these, the most frequently reported consequences were to research integrity (52% of articles), research capacity (36% of articles) and running low-value trials (34% of articles). 254 articles provided recommendations to reduce trial costs; of these, the most frequently reported recommendations related to improvements in: operational efficiencies (33% of articles); patient accrual (24% of articles); funding for trials and transparency in trials reporting (18% of articles, each). CONCLUSION Key findings from the review are: 1) delayed trial activation has costs to budgets and research; 2) poor accrual leads to low-value trials and wasted resources; 3) the pharmaceutical industry can be a pragmatic, if problematic, partner in clinical research; 4) organizational know-how and successful research collaboration are benefits of network/cooperative groups; and 5) there are spillover benefits of clinical trials to healthcare systems, including better health outcomes, enhanced research capacity, and drug cost avoidance. There is a need for more economic evaluations of the benefits of clinical research, such as health system use (or avoidance) and health outcomes in cities and health authorities with institutions that conduct clinical research, to demonstrate the affordability of clinical trials, despite their high cost.
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Affiliation(s)
- Colene Bentley
- 1 Department of Cancer Control Research, BC Cancer, Vancouver, BC, Canada.,2 Canadian Centre for Applied Research in Cancer Control, Vancouver, BC, Canada
| | - Sonya Cressman
- 1 Department of Cancer Control Research, BC Cancer, Vancouver, BC, Canada.,2 Canadian Centre for Applied Research in Cancer Control, Vancouver, BC, Canada
| | - Kim van der Hoek
- 1 Department of Cancer Control Research, BC Cancer, Vancouver, BC, Canada.,2 Canadian Centre for Applied Research in Cancer Control, Vancouver, BC, Canada
| | - Karen Arts
- 3 Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Janet Dancey
- 3 Ontario Institute for Cancer Research, Toronto, ON, Canada.,4 Department of Oncology, School of Medicine, Queen's University, Kingston, ON, Canada.,5 The National Cancer Institute of Canada Clinical Trials Group, Kingston, ON, Canada
| | - Stuart Peacock
- 1 Department of Cancer Control Research, BC Cancer, Vancouver, BC, Canada.,2 Canadian Centre for Applied Research in Cancer Control, Vancouver, BC, Canada.,6 Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
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Strech D, Sievers S, Märschenz S, Riedel N, Wieschowski S, Meerpohl J, Langhof H, Müller-Ohlraun S, Dirnagl U. Tracking the timely dissemination of clinical studies. Characteristics and impact of 10 tracking variables. F1000Res 2018; 7:1863. [PMID: 31131084 PMCID: PMC6518431 DOI: 10.12688/f1000research.17022.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/21/2018] [Indexed: 11/20/2022] Open
Abstract
Background: Several meta-research studies and benchmarking activities have assessed how comprehensively and timely, academic institutions and private companies publish their clinical studies. These current "clinical trial tracking" activities differ substantially in how they sample relevant studies, and how they follow up on their publication. Methods: To allow informed policy and decision making on future publication assessment and benchmarking of institutions and companies, this paper outlines and discusses 10 variables that influence the tracking of timely publications. Tracking variables were initially selected by experts and by the authors through discussion. To validate the completeness of our set of variables, we conducted i) an explorative review of tracking studies and ii) an explorative tracking of registered clinical trials of three leading German university medical centres. Results: We identified the following 10 relevant variables impacting the tracking of clinical studies: 1) responsibility for clinical studies, 2) type and characteristics of clinical studies, 3) status of clinical studies, 4) source for sampling, 5) timing of registration, 6) determination of completion date, 7) timeliness of dissemination, 8) format of dissemination, 9) source for tracking, and 10) inter-rater reliability. Based on the description of these tracking variables and their influence, we discuss which variables could serve in what ways as a standard assessment of "timely publication". Conclusions: To facilitate the tracking and consequent benchmarking of how often and how timely academic institutions and private companies publish clinical study results, we have two core recommendations. First, the improvement in the link between registration and publication, for example via institutional policies for academic institutions and private companies. Second, the comprehensive and transparent reporting of tracking studies according to the 10 variables presented in this paper.
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Affiliation(s)
- Daniel Strech
- QUEST Center, Berlin Institute of Health (BIH), Berlin, Germany
- Charité - Universitätsmedizin Berlin, Berlin, Germany
- Institute for History, Ethics and Philosophy of Medicine, Hannover Medical School, Hannover, Germany
| | - Sören Sievers
- Institute for History, Ethics and Philosophy of Medicine, Hannover Medical School, Hannover, Germany
| | - Stefanie Märschenz
- NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Nico Riedel
- QUEST Center, Berlin Institute of Health (BIH), Berlin, Germany
| | - Susanne Wieschowski
- Institute for History, Ethics and Philosophy of Medicine, Hannover Medical School, Hannover, Germany
| | - Jörg Meerpohl
- Institute for Evidence in Medicine (for Cochrane Germany Foundation), Medical Center – University of Freiburg, Faculty of Medicine, Freiburg, Germany
| | - Holger Langhof
- QUEST Center, Berlin Institute of Health (BIH), Berlin, Germany
- Charité - Universitätsmedizin Berlin, Berlin, Germany
- Institute for History, Ethics and Philosophy of Medicine, Hannover Medical School, Hannover, Germany
| | | | - Ulrich Dirnagl
- QUEST Center, Berlin Institute of Health (BIH), Berlin, Germany
- Charité - Universitätsmedizin Berlin, Berlin, Germany
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McLeroy KR, Garney W, Mayo-Wilson E, Grant S. Scientific Reporting: Raising the Standards. HEALTH EDUCATION & BEHAVIOR 2018; 43:501-8. [PMID: 27624441 DOI: 10.1177/1090198116668522] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This article is based on a presentation that was made at the 2014 annual meeting of the editorial board of Health Education & Behavior. The article addresses critical issues related to standards of scientific reporting in journals, including concerns about external and internal validity and reporting bias. It reviews current reporting guidelines, effects of adopting guidelines, and offers suggestions for improving reporting. The evidence about the effects of guideline adoption and implementation is briefly reviewed. Recommendations for adoption and implementation of appropriate guidelines, including considerations for journals, are provided.
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Affiliation(s)
| | | | - Evan Mayo-Wilson
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Enhancing primary reports of randomized controlled trials: Three most common challenges and suggested solutions. Proc Natl Acad Sci U S A 2018. [PMID: 29531032 DOI: 10.1073/pnas.1708286114] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Evidence from a well-designed randomized controlled trial (RCT) is generally considered to be the gold standard that can inform clinical practice and guide decision-making. However, several deficiencies in the reporting of RCTs have frequently been identified, including incomplete, selective, and biased or inconsistent reporting. Such suboptimal reporting may lead to irreproducible results, substantial waste of resources, impaired study validity, erosion of public trust in science, and a high risk of research misconduct. In this article, we present an overview of the reporting of RCTs in the biomedical literature with a focus on the three most common reporting problems: (i) lack of adherence to reporting guidelines, (ii) inconsistencies between trial protocols or registrations and full reports, and (iii) inconsistencies between abstracts and their corresponding full reports. Unsatisfactory levels of adherence to guidelines and frequent inconsistencies between protocols or registrations and full reports, and between abstracts and full reports, were consistently found in various biomedical research fields. A variety of factors were found to be associated with these reporting challenges. Improved reporting can build public trust and credibility of science, save resources, and enhance the ethical integrity of research. Therefore, joint efforts from the various sectors of the biomedical community (researchers, journal editors and reviewers, educators, healthcare providers, and other research consumers) are needed to reduce and reverse the current suboptimal state of RCT reporting in the literature.
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McCarthy O, Ahamed I, Kulaeva F, Tokhirov R, Saibov S, Vandewiele M, Standaert S, Leurent B, Edwards P, Palmer M, Free C. A randomized controlled trial of an intervention delivered by mobile phone app instant messaging to increase the acceptability of effective contraception among young people in Tajikistan. Reprod Health 2018; 15:28. [PMID: 29433506 PMCID: PMC5809875 DOI: 10.1186/s12978-018-0473-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 02/05/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Unintended pregnancy is associated with poorer health outcomes for women and their families. In Tajikistan, around 26% of married 15-24 year old women have an unmet need for contraception. There is some evidence that interventions delivered by mobile phone can affect contraceptive-related behaviour and knowledge. We developed an intervention delivered by mobile phone app instant messaging to improve acceptability of effective contraceptive methods among young people in Tajikistan. METHODS This was a randomized controlled trial among Tajik people aged 16-24. Participants allocated to the intervention arm had access to an app plus intervention messages. Participants allocated to the control arm had access to the app plus control messages. The primary outcome was acceptability of at least one method of effective contraception at 4 months. Secondary outcomes were use of effective contraception at 4 months and during the study, acceptability of individual methods, service uptake, unintended pregnancy and induced abortion. Process outcomes were knowledge, perceived norms, personal agency and intention. Outcomes were analysed using logistic and linear regression. We conducted a pre-specified subgroup analysis and a post-hoc analysis of change in acceptability from baseline to follow-up. RESULTS Five hundred and seventy-three participants were enrolled. Intervention content was included on the app, causing contamination. Four hundred and seventy-two (82%) completed follow-up for the primary outcome. There was no evidence of a difference in acceptability of effective contraception between the groups (66% in the intervention arm vs 64% in the control arm, adjusted OR 1.21, 95% CI .80-1.83, p = 0.36). There were no differences in the secondary or process outcomes between groups. There was some evidence that the effect of the intervention was greater among women compared to men (interaction test p = 0.03). There was an increase in acceptability of effective contraception from baseline to follow-up (2% to 65%, p < 0.001). CONCLUSIONS The whole intervention delivered by instant messaging provided no additional benefit over a portion of the intervention delivered by app pages. The important increase in contraceptive acceptability from baseline to follow-up suggests that the intervention content included on the app may influence attitudes. Further research is needed to establish the effect of the intervention on attitudes towards and use of effective contraception among married/sexually active young people. TRIAL REGISTRATION Clinicaltrial.gov NCT02905513 . Date of registration: 14 September 2016.
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Affiliation(s)
- Ona McCarthy
- Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
| | - Irrfan Ahamed
- Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Firuza Kulaeva
- Tajik Family Planning Association, 10 Rudaki Avenue, TC 'Sadbarg', 7th floor, Dushanbe, Tajikistan
| | - Ravshan Tokhirov
- Tajik Family Planning Association, 10 Rudaki Avenue, TC 'Sadbarg', 7th floor, Dushanbe, Tajikistan
| | - Salokhiddin Saibov
- Tajik Family Planning Association, 10 Rudaki Avenue, TC 'Sadbarg', 7th floor, Dushanbe, Tajikistan
| | - Marieka Vandewiele
- International Planned Parenthood Federation European Network, Rue Royale 146, 1000, Brussels, Belgium
| | - Sarah Standaert
- International Planned Parenthood Federation European Network, Rue Royale 146, 1000, Brussels, Belgium
| | - Baptiste Leurent
- Department of Medical Statistics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Phil Edwards
- Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Melissa Palmer
- Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Caroline Free
- Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
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35
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Li G, Kamel M, Jin Y, Xu MK, Mbuagbaw L, Samaan Z, Levine MA, Thabane L. Exploring the characteristics, global distribution and reasons for retraction of published articles involving human research participants: a literature survey. J Multidiscip Healthc 2018; 11:39-47. [PMID: 29403283 PMCID: PMC5779311 DOI: 10.2147/jmdh.s151745] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Aim Article retraction is a measure taken by journals or authors where there is evidence of research misconduct or error, redundancy, plagiarism or unethical research. Recently, the retraction of scientific publications has been on the rise. In this survey, we aimed to describe the characteristics and distribution of retracted articles and the reasons for retractions. Methods We searched retracted articles on the PubMed database and Retraction Watch website from 1980 to February 2016. The primary outcomes were the characteristics and distribution of retracted articles and the reasons for retractions. The secondary outcomes included how article retractions were handled by journals and how to improve the journal practices toward article retractions. Results We included 1,339 retracted articles. Most retracted articles had six authors or fewer. Article retraction was most common in the USA (26%), Japan (11%) and Germany (10%). The main reasons for article retraction were misconduct (51%, n = 685) and error (14%, n = 193). There were 66% (n = 889) of retracted articles having male senior or corresponding authors. Of the articles retracted after August 2010, 63% (n = 567) retractions were reported on Retraction Watch. Large discrepancies were observed in the ways that different journals handled article retractions. For instance, articles were completely withdrawn from some journals, while in others, articles were still available with no indication of retraction. Likewise, some retraction notices included a detailed account of the events that led to article retraction, while others only consisted of a statement indicating the article retraction. Conclusion The characteristics, geographic distribution and reasons for retraction of published articles involving human research participants were examined in this survey. More efforts are needed to improve the consistency and transparency of journal practices toward article retractions.
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Affiliation(s)
- Guowei Li
- Department of Health Research Methods, Evidence, and Impact.,St. Joseph's Healthcare Hamilton.,Centre for Evaluation of Medicines, Programs for Assessment of Technology in Health Research Institute
| | - Mariam Kamel
- Department of Health Research Methods, Evidence, and Impact
| | - Yanling Jin
- Department of Health Research Methods, Evidence, and Impact
| | | | - Lawrence Mbuagbaw
- Department of Health Research Methods, Evidence, and Impact.,St. Joseph's Healthcare Hamilton
| | - Zainab Samaan
- Department of Health Research Methods, Evidence, and Impact.,Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Mitchell Ah Levine
- Department of Health Research Methods, Evidence, and Impact.,St. Joseph's Healthcare Hamilton.,Centre for Evaluation of Medicines, Programs for Assessment of Technology in Health Research Institute.,Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Lehana Thabane
- Department of Health Research Methods, Evidence, and Impact.,St. Joseph's Healthcare Hamilton
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36
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Li G, Abbade LPF, Nwosu I, Jin Y, Leenus A, Maaz M, Wang M, Bhatt M, Zielinski L, Sanger N, Bantoto B, Luo C, Shams I, Shahid H, Chang Y, Sun G, Mbuagbaw L, Samaan Z, Levine MAH, Adachi JD, Thabane L. A systematic review of comparisons between protocols or registrations and full reports in primary biomedical research. BMC Med Res Methodol 2018; 18:9. [PMID: 29325533 PMCID: PMC5765717 DOI: 10.1186/s12874-017-0465-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Accepted: 12/21/2017] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Prospective study protocols and registrations can play a significant role in reducing incomplete or selective reporting of primary biomedical research, because they are pre-specified blueprints which are available for the evaluation of, and comparison with, full reports. However, inconsistencies between protocols or registrations and full reports have been frequently documented. In this systematic review, which forms part of our series on the state of reporting of primary biomedical, we aimed to survey the existing evidence of inconsistencies between protocols or registrations (i.e., what was planned to be done and/or what was actually done) and full reports (i.e., what was reported in the literature); this was based on findings from systematic reviews and surveys in the literature. METHODS Electronic databases, including CINAHL, MEDLINE, Web of Science, and EMBASE, were searched to identify eligible surveys and systematic reviews. Our primary outcome was the level of inconsistency (expressed as a percentage, with higher percentages indicating greater inconsistency) between protocols or registration and full reports. We summarized the findings from the included systematic reviews and surveys qualitatively. RESULTS There were 37 studies (33 surveys and 4 systematic reviews) included in our analyses. Most studies (n = 36) compared protocols or registrations with full reports in clinical trials, while a single survey focused on primary studies of clinical trials and observational research. High inconsistency levels were found in outcome reporting (ranging from 14% to 100%), subgroup reporting (from 12% to 100%), statistical analyses (from 9% to 47%), and other measure comparisons. Some factors, such as outcomes with significant results, sponsorship, type of outcome and disease speciality were reported to be significantly related to inconsistent reporting. CONCLUSIONS We found that inconsistent reporting between protocols or registrations and full reports of primary biomedical research is frequent, prevalent and suboptimal. We also identified methodological issues such as the need for consensus on measuring inconsistency across sources for trial reports, and more studies evaluating transparency and reproducibility in reporting all aspects of study design and analysis. A joint effort involving authors, journals, sponsors, regulators and research ethics committees is required to solve this problem.
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Affiliation(s)
- Guowei Li
- Department of Health Research Methods, Evidence, and Impact, McMaster University, St. Joseph's Healthcare, Hamilton, 501-25 Charlton Avenue East, Hamilton, ON, L8N 1Y2, Canada. .,St. Joseph's Healthcare Hamilton, McMaster University, Hamilton, ON, Canada. .,Centre for Evaluation of Medicines, Programs for Assessment of Technology in Health (PATH) Research Institute, McMaster University, Hamilton, ON, Canada.
| | - Luciana P F Abbade
- Department of Dermatology and Radiotherapy, Botucatu Medical School, Universidade Estadual Paulista, UNESP, São Paulo, Brazil
| | - Ikunna Nwosu
- Department of Health Research Methods, Evidence, and Impact, McMaster University, St. Joseph's Healthcare, Hamilton, 501-25 Charlton Avenue East, Hamilton, ON, L8N 1Y2, Canada
| | - Yanling Jin
- Department of Health Research Methods, Evidence, and Impact, McMaster University, St. Joseph's Healthcare, Hamilton, 501-25 Charlton Avenue East, Hamilton, ON, L8N 1Y2, Canada
| | - Alvin Leenus
- Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Muhammad Maaz
- Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Mei Wang
- Department of Health Research Methods, Evidence, and Impact, McMaster University, St. Joseph's Healthcare, Hamilton, 501-25 Charlton Avenue East, Hamilton, ON, L8N 1Y2, Canada
| | - Meha Bhatt
- Department of Health Research Methods, Evidence, and Impact, McMaster University, St. Joseph's Healthcare, Hamilton, 501-25 Charlton Avenue East, Hamilton, ON, L8N 1Y2, Canada
| | - Laura Zielinski
- McMaster Integrative Neuroscience Discovery and Study, McMaster University, Hamilton, ON, Canada
| | - Nitika Sanger
- Medical Sciences, McMaster University, Hamilton, ON, Canada
| | - Bianca Bantoto
- Integrated Sciences, McMaster University, Hamilton, ON, Canada
| | - Candice Luo
- Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Ieta Shams
- Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, ON, Canada
| | - Hamnah Shahid
- Arts and Science, McMaster University, Hamilton, ON, Canada
| | - Yaping Chang
- Department of Health Research Methods, Evidence, and Impact, McMaster University, St. Joseph's Healthcare, Hamilton, 501-25 Charlton Avenue East, Hamilton, ON, L8N 1Y2, Canada
| | - Guangwen Sun
- Department of Health Research Methods, Evidence, and Impact, McMaster University, St. Joseph's Healthcare, Hamilton, 501-25 Charlton Avenue East, Hamilton, ON, L8N 1Y2, Canada
| | - Lawrence Mbuagbaw
- Department of Health Research Methods, Evidence, and Impact, McMaster University, St. Joseph's Healthcare, Hamilton, 501-25 Charlton Avenue East, Hamilton, ON, L8N 1Y2, Canada.,St. Joseph's Healthcare Hamilton, McMaster University, Hamilton, ON, Canada
| | - Zainab Samaan
- Department of Health Research Methods, Evidence, and Impact, McMaster University, St. Joseph's Healthcare, Hamilton, 501-25 Charlton Avenue East, Hamilton, ON, L8N 1Y2, Canada.,Department of Medicine, McMaster University, Hamilton, Canada
| | - Mitchell A H Levine
- Department of Health Research Methods, Evidence, and Impact, McMaster University, St. Joseph's Healthcare, Hamilton, 501-25 Charlton Avenue East, Hamilton, ON, L8N 1Y2, Canada.,St. Joseph's Healthcare Hamilton, McMaster University, Hamilton, ON, Canada.,Centre for Evaluation of Medicines, Programs for Assessment of Technology in Health (PATH) Research Institute, McMaster University, Hamilton, ON, Canada.,Department of Medicine, McMaster University, Hamilton, Canada
| | - Jonathan D Adachi
- Department of Health Research Methods, Evidence, and Impact, McMaster University, St. Joseph's Healthcare, Hamilton, 501-25 Charlton Avenue East, Hamilton, ON, L8N 1Y2, Canada.,St. Joseph's Healthcare Hamilton, McMaster University, Hamilton, ON, Canada.,Department of Medicine, McMaster University, Hamilton, Canada
| | - Lehana Thabane
- Department of Health Research Methods, Evidence, and Impact, McMaster University, St. Joseph's Healthcare, Hamilton, 501-25 Charlton Avenue East, Hamilton, ON, L8N 1Y2, Canada. .,St. Joseph's Healthcare Hamilton, McMaster University, Hamilton, ON, Canada. .,Department of Health Research Methods, Evidence, and Impact, McMaster University, Father Sean O'Sullivan Research Centre, St. Joseph's Healthcare Hamilton, 3rd Floor Martha, Room H325, 50 Charlton Avenue E, Hamilton, ON, L8N 4A6, Canada.
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McCarthy OL, Osorio Calderon V, Makleff S, Huaynoca S, Leurent B, Edwards P, Lopez Gallardo J, Free C. An Intervention Delivered by App Instant Messaging to Increase Acceptability and Use of Effective Contraception Among Young Women in Bolivia: Protocol of a Randomized Controlled Trial. JMIR Res Protoc 2017; 6:e252. [PMID: 29254910 PMCID: PMC5748473 DOI: 10.2196/resprot.8679] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 10/20/2017] [Accepted: 11/02/2017] [Indexed: 11/17/2022] Open
Abstract
Background Unintended pregnancy is associated with numerous poorer health outcomes for both women and their children. Fulfilling unmet need for contraception is essential in avoiding unintended pregnancies, yet millions of women in low- and middle-income countries continue to face obstacles in realizing their fertility desires. In Bolivia, family planning progress has improved in recent decades but lags behind other countries in the region. Unmet need for contraception among women aged 15 to 19 years is estimated to be 38%, with the adolescent fertility rate at 70 per 1000 women. Mobile phones are an established and popular mode in which to deliver health behavior support. The London School of Hygiene & Tropical Medicine and the Centro de Investigación, Educación y Servicios in Bolivia have partnered to develop and evaluate a contraceptive behavioral intervention for Bolivian young women delivered by mobile phone. The intervention was developed guided by behavioral science and consists of short instant messages sent through an app over 4 months. Objective The objective of this study is to evaluate the effect of the intervention on young women’s use of and attitudes toward the most effective contraceptive methods. Methods We will allocate 1310 women aged 16 to 24 years with an unmet need for contraception in a 1:1 ratio to receive the intervention messages or the control messages about trial participation. The messages are sent through the Tú decides app, which contains standard family planning information. Coprimary outcomes are use and acceptability of at least one effective contraceptive method, both measured at 4 months. Results Recruitment commenced on March 1, 2017 and was completed on July 29, 2017. We estimate that the follow-up period will end in January 2018. Conclusions This trial will evaluate the effect of the intervention on young women’s use of and attitudes toward the (nonpermanent) effective contraception methods available in Bolivia. Trial Registration ClinicalTrials.gov NCT02905526; https://clinicaltrials.gov/ct2/show/NCT02905526 (Archived by WebCite at http://www.webcitation.org/6vT0yIFfN)
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Affiliation(s)
- Ona L McCarthy
- The London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Veronica Osorio Calderon
- Centro de Investigación, Educación y Servicios - Salud Sexual Salud Reproductiva, La Paz, Bolivia
| | - Shelly Makleff
- International Planned Parenthood Federation/Western Hemisphere Region, New York, NY, United States
| | - Silvia Huaynoca
- International Planned Parenthood Federation/Western Hemisphere Region, New York, NY, United States
| | - Baptiste Leurent
- The London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Phil Edwards
- The London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Jhonny Lopez Gallardo
- Centro de Investigación, Educación y Servicios - Salud Sexual Salud Reproductiva, La Paz, Bolivia
| | - Caroline Free
- The London School of Hygiene and Tropical Medicine, London, United Kingdom
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McCarthy OL, Wazwaz O, Jado I, Leurent B, Edwards P, Adada S, Stavridis A, Free C. An intervention delivered by text message to increase the acceptability of effective contraception among young women in Palestine: study protocol for a randomised controlled trial. Trials 2017; 18:454. [PMID: 28974258 PMCID: PMC5627444 DOI: 10.1186/s13063-017-2191-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 09/14/2017] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Unintended pregnancy can negatively impact women's lives and is associated with poorer health outcomes for women and children. Many women, particularly in low- and middle-income countries, continue to face obstacles in avoiding unintended pregnancy. In the State of Palestine, a survey conducted in 2006 estimated that 38% of pregnancies are unintended. In 2014, unmet need for contraception was highest among young women aged 20-24 years, at 15%. Mobile phones are increasingly being used to deliver health support. Once developed, interventions delivered by mobile phone are often cheaper to deliver than face-to-face support. The London School of Hygiene and Tropical Medicine and the Palestinian Family Planning and Protection Association have partnered to develop and evaluate a contraceptive behavioural intervention for young women in Palestine delivered by mobile phone. The intervention was developed guided by behavioural science and consists of short, mobile phone text messages that contain information about contraception and behaviour change methods delivered over 4 months. METHODS We will evaluate the intervention by conducting a randomised controlled trial. Five hundred and seventy women aged 18-24 years, who do not report using an effective method of contraception, will be allocated with a 1:1 ratio to receive the intervention text messages or control text messages about trial participation. The primary outcome is self-reported acceptability of at least one method of effective contraception at 4 months. Secondary outcomes include the use of effective contraception, acceptability of individual methods, discontinuation, service uptake, unintended pregnancy and abortion. Process outcomes include knowledge, perceived norms, personal agency and intervention dose received. Outcomes at 4 months will be compared between arms using logistic regression. DISCUSSION This trial will determine the effect of the intervention on young women's attitudes towards the most effective methods of contraception. If the intervention is found to be effective, the intervention will be implemented widely across Palestine. The results could also be used to design a larger trial to establish its effect on unintended pregnancy. TRIAL REGISTRATION ClinicalTrials.gov, ID: NCT02905461 . Registered on 14 September 2016.
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Affiliation(s)
- Ona L McCarthy
- Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT UK
| | - Ola Wazwaz
- Palestinian Family Planning and Protection Association, Industrial Zone, Wadi Al-Joze, Jerusalem, Palestine
| | - Iman Jado
- Palestinian Family Planning and Protection Association, Industrial Zone, Wadi Al-Joze, Jerusalem, Palestine
| | - Baptiste Leurent
- Department of Medical Statistics, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT UK
| | - Phil Edwards
- Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT UK
| | - Samia Adada
- International Planned Parenthood Federation, Arab World Regional Office, 2 Place Virgile, Notre Dame, Tunis 1082 Tunisia
| | - Amina Stavridis
- Palestinian Family Planning and Protection Association, Industrial Zone, Wadi Al-Joze, Jerusalem, Palestine
| | - Caroline Free
- Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT UK
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Love RE, Adams J, van Sluijs EMF. Equity effects of children's physical activity interventions: a systematic scoping review. Int J Behav Nutr Phys Act 2017; 14:134. [PMID: 28969638 PMCID: PMC5625682 DOI: 10.1186/s12966-017-0586-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Accepted: 09/13/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Differential effects of physical activity (PA) interventions across population sub-groups may contribute to inequalities in health. This systematic scoping review explored the state of the evidence on equity effects in response to interventions targeting children's PA promotion. The aims were to assess and summarise the availability of evidence on differential intervention effects of children's PA interventions across gender, body mass index, socioeconomic status, ethnicity, place of residence and religion. METHODS Using a pre-piloted search strategy, six electronic databases were searched for controlled intervention trials, aiming to increase PA in children (6-18 years of age), that used objective forms of measurement. Screening and data extraction were conducted in duplicate. Reporting of analyses of differential effects were summarized for each equity characteristic and logistic regression analyses run to investigate intervention characteristics associated with the reporting of equity analyses. RESULTS The literature search identified 13,052 publications and 7963 unique records. Following a duplicate screening process 125 publications representing 113 unique intervention trials were included. Although the majority of trials collected equity characteristics at baseline, few reported differential effects analyses across the equity factors of interest. All 113 included interventions reported gender at baseline with 46% of non-gender targeted interventions reporting differential effect analyses by gender. Respective figures were considerably smaller for body mass index, socioeconomic status, ethnicity, place of residence and religion. There was an increased likelihood of studying differential effects in school based interventions (OR: 2.9 [1.2-7.2]) in comparison to interventions in other settings, larger studies (per increase in 100 participants; 1.2 [1.0 - 1.4]); and where a main intervention effect on objectively measured PA was reported (3.0 [1.3-6.8]). CONCLUSIONS Despite regularly collecting relevant information at baseline, most controlled trials of PA interventions in children do not report analyses of differences in intervention effect across outlined equity characteristics. Consequently, there is a scarcity of evidence concerning the equity effects of these interventions, particularly beyond gender, and a lack of understanding of subgroups that may benefit from, or be disadvantaged by, current intervention efforts. Further evidence synthesis and primary research is needed to effectively understand the impact of PA interventions on existing behavioural inequalities within population subgroups of children. TRIAL REGISTRATION PROSPERO (PROSPERO 2016: CRD42016034020 ).
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Affiliation(s)
- Rebecca E. Love
- Centre for Diet and Activity Research (CEDAR), MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ UK
| | - Jean Adams
- Centre for Diet and Activity Research (CEDAR), MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ UK
| | - Esther M. F. van Sluijs
- Centre for Diet and Activity Research (CEDAR), MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ UK
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40
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McCarthy O, Leurent B, Edwards P, Tokhirov R, Free C. A randomised controlled trial of an intervention delivered by app instant messaging to increase the acceptability of effective contraception among young people in Tajikistan: study protocol. BMJ Open 2017; 7:e017606. [PMID: 28939582 PMCID: PMC5623472 DOI: 10.1136/bmjopen-2017-017606] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
INTRODUCTION Women in lower income countries experience unintended pregnancies at a higher rate compared with women in higher income countries. Unintended pregnancy is associated with numerous poorer health outcomes for both women and their children. In Tajikistan, an estimated 26% of married individuals aged 15-24 years have an unmet need for contraception. The strong cultural value placed on childbearing and oppositional attitudes towards contraception are major barriers to contraceptive uptake in the country.Mobile phone ownership is widespread in Tajikistan. The option of receiving reproductive health support on your personal phone may be an appealing alternative to attending a clinic, particularly for young people. The London School of Hygiene & Tropical Medicine and the Tajik Family Planning Association have partnered to develop and evaluate a contraceptive behavioural intervention delivered by mobile phone. The intervention was developed in 2015-2016 guided by behavioural science. It consists of short instant messages sent through an app over 4 months, contains information about contraception and behaviour change methods. METHODS AND ANALYSIS This randomised controlled trial is designed to evaluate the effect of the intervention on self-reported acceptability of effective contraception at 4 months. 570 men and women aged 16-24 years will be allocated with a ratio of 1:1 to receive the intervention messages or the control messages about trial participation. The messages will be sent through the Tajik Family Planning Association's 'healthy lifestyles' app, which contains basic information about contraception. ETHICS AND DISSEMINATION The trial was granted ethical approval by the London School of Hygiene & Tropical Medicine Interventions Research Ethics Committee on 16 May 2016 and by the Tajik National Scientific and Research Centre on Paediatrics and Child Surgery on 15 April 2016. The results of the trial will be submitted for publication in peer-reviewed academic journals and disseminated to study stakeholders. TRIAL REGISTRATION NUMBER Clinicaltrial.gov NCT02905513. DATE OF REGISTRATION 14 September 2016. WHO TRIAL REGISTRATION DATASET: http://apps.who.int/trialsearch/Trial2.aspx?TrialID=NCT02905513.
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Affiliation(s)
- Ona McCarthy
- Department of Population Health, The London School of Hygiene and Tropical Medicine, London, UK
| | - Baptiste Leurent
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Phil Edwards
- Department of Population Health, The London School of Hygiene and Tropical Medicine, London, UK
| | | | - Caroline Free
- Department of Population Health, The London School of Hygiene and Tropical Medicine, London, UK
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Abstract
INTRODUCTION Growing evidence points for the need to publish study protocols in the health field. The aim of this paper was to observe whether the growing interest in publishing study protocols in the broader health field has been translated into increased publications of rehabilitation study protocols. EVIDENCE ACQUISITION PubMed was searched with appropriate combinations of Medical Subject Headings up to December 2014. The effective presence of study protocols was manually screened. Regression models analyzed the yearly growth of publications. Two-sample Z-tests analyzed whether the proportion of systematic reviews (SRs) and randomized controlled trials (RCTs) among study protocols differed from that of the same designs for the broader rehabilitation research. EVIDENCE SYNTHESIS Up to December 2014, 746 publications of rehabilitation study protocols were identified, with an exponential growth since 2005 (r2=0.981; P<0.001). RCT protocols were the most common among rehabilitation study protocols (83%), while RCTs were significantly more prevalent among study protocols than among the broader rehabilitation research (83% vs. 35.8%; P<0.001). For SRs, the picture was reversed: significantly less common among study protocols (2.8% vs. 9.3%; P<0.001). Funding was more often reported by rehabilitation study protocols than the broader rehabilitation research (90% vs. 53.1%; P<0.001). Rehabilitation journals published a significantly lower share of rehabilitation study protocols than they did for the broader rehabilitation research (1.8% vs.16.7%; P<0.001). CONCLUSIONS Identifying the reasons for these discrepancies and reverting unwarranted disparities (e.g. low rate of publication for rehabilitation SR protocols) are likely new avenues for rehabilitation research and its publication. SRs, particularly those aggregating RCT results, are considered the best standard of evidence to guide rehabilitation clinical practice; however, that standard can be improved in rigor and/or transparency if the publications of rehabilitation SRs protocols become more common.
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Affiliation(s)
- Tiago S Jesus
- Global Health and Tropical Medicine (GHTM) & WHO Collaborating Centre for Health Workforce Policy and Planning, Institute of Hygiene and Tropical Medicine, NOVA University of Lisbon, Lisbon, Portugal -
| | - Heather L Colquhoun
- Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON, Canada.,Rehabilitation Sciences Institute (RSI), University of Toronto, Toronto, ON, Canada
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Yiu ZZ, Warren RB. Raising Standards for the Evaluation of Future Psoriasis Therapeutics: A Critical Checklist. Clin Pharmacol Ther 2017; 102:642-648. [DOI: 10.1002/cpt.788] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Revised: 06/20/2017] [Accepted: 07/06/2017] [Indexed: 12/15/2022]
Affiliation(s)
- Zenas Z.N. Yiu
- Dermatology Centre; Salford Royal NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, NIHR Manchester Biomedical Research Centre; Manchester UK
| | - Richard B. Warren
- Dermatology Centre; Salford Royal NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, NIHR Manchester Biomedical Research Centre; Manchester UK
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Nicod E, Berg Brigham K, Durand-Zaleski I, Kanavos P. Dealing with Uncertainty and Accounting for Social Value Judgments in Assessments of Orphan Drugs: Evidence from Four European Countries. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2017; 20:919-926. [PMID: 28712621 DOI: 10.1016/j.jval.2017.03.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 02/27/2017] [Accepted: 03/03/2017] [Indexed: 06/07/2023]
Abstract
OBJECTIVES To better understand the reasons for differences in reimbursement decisions for orphan drugs in four European countries that were not readily apparent from health technology assessment (HTA) reports and operating procedures. METHODS Semistructured interviews with representatives of HTA bodies in England, Scotland, Sweden, and France were conducted. An interview topic guide was developed on the basis of findings from a systematic comparison of HTA decisions for 10 orphan drugs. Qualitative thematic data analysis was applied to the interview transcripts using the framework approach. RESULTS Eight representatives from the four HTA bodies were interviewed between March and June 2015. Evidentiary requirements and approaches to dealing with imperfect or incomplete evidence were explored, including trial design and duration, study population and subgroups, comparators, and end points. Interviewees agreed that decisions regarding orphan drugs are made in a context of lower quality evidence, and the threshold of acceptable uncertainty varied by country. Some countries imposed higher evidentiary standards for greater clinical claims, which may be more challenging for orphan diseases. The acceptability of surrogate end points was not consistent across countries nor were the validation requirements. The most common social value judgments identified related to innovation, disease severity, and unmet need. Differences were seen in the way these concepts were defined and accounted for across countries. CONCLUSIONS Although agreement was seen in evidentiary requirements or preferences, there were subtle differences in the circumstances in which uncertain evidence may be considered acceptable, possibly explaining differences in HTA recommendations across countries.
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Affiliation(s)
- Elena Nicod
- Department of Social Policy, LSE Health, London School of Economics and Political Science, London, UK; Center for Research on Health and Social Care Management, Bocconi University, Milan, Italy.
| | - Karen Berg Brigham
- Université Paris Est Créteil Val de Marne (UPEC), Créteil, France; URC Eco Ile-de-France (AP-HP), Paris, France
| | - Isabelle Durand-Zaleski
- Université Paris Est Créteil Val de Marne (UPEC), Créteil, France; URC Eco Ile-de-France (AP-HP), Paris, France; ECEVE UMRS 1123, UEC-Hôpital Robert Debré (AP-HP), Paris, France
| | - Panos Kanavos
- Department of Social Policy, LSE Health, London School of Economics and Political Science, London, UK
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Wayant C, Scheckel C, Hicks C, Nissen T, Leduc L, Som M, Vassar M. Evidence of selective reporting bias in hematology journals: A systematic review. PLoS One 2017; 12:e0178379. [PMID: 28570573 PMCID: PMC5453439 DOI: 10.1371/journal.pone.0178379] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2017] [Accepted: 05/11/2017] [Indexed: 01/10/2023] Open
Abstract
Introduction Selective reporting bias occurs when chance or selective outcome reporting rather than the intervention contributes to group differences. The prevailing concern about selective reporting bias is the possibility of results being modified towards specific conclusions. In this study, we evaluate randomized controlled trials (RCTs) published in hematology journals, a group in which selective outcome reporting has not yet been explored. Methods Our primary goal was to examine discrepancies between the reported primary and secondary outcomes in registered and published RCTs concerning hematological malignancies reported in hematology journals with a high impact factor. The secondary goals were to address whether outcome reporting discrepancies favored statistically significant outcomes, whether a pattern existed between the funding source and likelihood of outcome reporting bias, and whether temporal trends were present in outcome reporting bias. For trials with major outcome discrepancies, we contacted trialists to determine reasons for these discrepancies. Trials published between January 1, 2010 and December 31, 2015 in Blood; British Journal of Haematology; American Journal of Hematology; Leukemia; and Haematologica were included. Results Of 499 RCTs screened, 109 RCTs were included. Our analysis revealed 118 major discrepancies and 629 total discrepancies. Among the 118 discrepancies, 30 (25.4%) primary outcomes were demoted, 47 (39.8%) primary outcomes were omitted, and 30 (25.4%) primary outcomes were added. Three (2.5%) secondary outcomes were upgraded to a primary outcome. The timing of assessment for a primary outcome changed eight (6.8%) times. Thirty-one major discrepancies were published with a P-value and twenty-five (80.6%) favored statistical significance. A majority of authors whom we contacted cited a pre-planned subgroup analysis as a reason for outcome changes. Conclusion Our results suggest that outcome changes occur frequently in hematology trials. Because RCTs ultimately underpin clinical judgment and guide policy implementation, selective reporting could pose a threat to medical decision making.
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Affiliation(s)
- Cole Wayant
- Department of Institutional Research, Oklahoma State University Center for Health Sciences, Tulsa, Oklahoma, United States of America
- * E-mail:
| | - Caleb Scheckel
- Internal Medicine, Mayo Clinic, Scottsdale, Arizona, United States of America
| | - Chandler Hicks
- Department of Institutional Research, Oklahoma State University Center for Health Sciences, Tulsa, Oklahoma, United States of America
| | - Timothy Nissen
- Department of Institutional Research, Oklahoma State University Center for Health Sciences, Tulsa, Oklahoma, United States of America
| | - Linda Leduc
- Internal Medicine, Oklahoma State University Medical Center, Tulsa, Oklahoma, United States of America
| | - Mousumi Som
- Internal Medicine, Oklahoma State University Medical Center, Tulsa, Oklahoma, United States of America
| | - Matt Vassar
- Department of Institutional Research, Oklahoma State University Center for Health Sciences, Tulsa, Oklahoma, United States of America
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Primary endpoint discrepancies were found in one in ten clinical drug trials. Results of an inception cohort study. J Clin Epidemiol 2017; 89:199-208. [PMID: 28535887 DOI: 10.1016/j.jclinepi.2017.05.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Revised: 04/26/2017] [Accepted: 05/15/2017] [Indexed: 01/02/2023]
Abstract
OBJECTIVE To identify the occurrence and determinants of protocol-publication discrepancies in clinical drug trials. STUDY DESIGN AND SETTING All published clinical drug trials reviewed by the Dutch institutional review boards in 2007 were analyzed. Discrepancies between trial protocols and publications were measured among key reporting aspects. We evaluated the association of trial characteristics with discrepancies in primary endpoints by calculating the risk ratio (RR) and 95% confidence interval (CI). RESULTS Of the 334 published trials, 32 (9.6%) had a protocol/publication discrepancy in the primary endpoints. Among the subgroup of randomized controlled trials (RCTs; N = 204), 12 (5.9%) had a discrepancy in the primary endpoint. Investigator-initiated trials with and without industry (co-) funding were associated with having discrepancies in the primary endpoints compared with industry-sponsored trials (RR 3.7; 95% CI 1.4-9.9 and RR 4.4; 95% CI 2.0-9.5, respectively). Furthermore, other than phase 1-4 trials (vs. phase 1; RR 4.6; 95% CI 1.1-19.3), multicenter trials were also conducted outside the European Union (vs. single center; RR 0.2; 95% CI 0.1-0.6), not prospectively registered trials (RR 3.3; 95% CI 1.5-7.5), non-RCTs (vs. superiority RCT; RR 2.4; 95% CI 1.2-4.8) and, among the RCTs, crossover compared with a parallel group design (RR 3.7; 95% CI 1.1-12.3) were significantly associated with having discrepancies in the primary endpoints. CONCLUSIONS Improvement in completeness of reporting is still needed, especially among investigator-initiated trials and non-RCTs. To eliminate undisclosed discrepancies, trial protocols should be available in the public domain at the same time when the trial is published.
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Wallach JD, Sullivan PG, Trepanowski JF, Sainani KL, Steyerberg EW, Ioannidis JPA. Evaluation of Evidence of Statistical Support and Corroboration of Subgroup Claims in Randomized Clinical Trials. JAMA Intern Med 2017; 177:554-560. [PMID: 28192563 PMCID: PMC6657347 DOI: 10.1001/jamainternmed.2016.9125] [Citation(s) in RCA: 86] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Many published randomized clinical trials (RCTs) make claims for subgroup differences. Objective To evaluate how often subgroup claims reported in the abstracts of RCTs are actually supported by statistical evidence (P < .05 from an interaction test) and corroborated by subsequent RCTs and meta-analyses. Data Sources This meta-epidemiological survey examines data sets of trials with at least 1 subgroup claim, including Subgroup Analysis of Trials Is Rarely Easy (SATIRE) articles and Discontinuation of Randomized Trials (DISCO) articles. We used Scopus (updated July 2016) to search for English-language articles citing each of the eligible index articles with at least 1 subgroup finding in the abstract. Study Selection Articles with a subgroup claim in the abstract with or without evidence of statistical heterogeneity (P < .05 from an interaction test) in the text and articles attempting to corroborate the subgroup findings. Data Extraction and Synthesis Study characteristics of trials with at least 1 subgroup claim in the abstract were recorded. Two reviewers extracted the data necessary to calculate subgroup-level effect sizes, standard errors, and the P values for interaction. For individual RCTs and meta-analyses that attempted to corroborate the subgroup findings from the index articles, trial characteristics were extracted. Cochran Q test was used to reevaluate heterogeneity with the data from all available trials. Main Outcomes and Measures The number of subgroup claims in the abstracts of RCTs, the number of subgroup claims in the abstracts of RCTs with statistical support (subgroup findings), and the number of subgroup findings corroborated by subsequent RCTs and meta-analyses. Results Sixty-four eligible RCTs made a total of 117 subgroup claims in their abstracts. Of these 117 claims, only 46 (39.3%) in 33 articles had evidence of statistically significant heterogeneity from a test for interaction. In addition, out of these 46 subgroup findings, only 16 (34.8%) ensured balance between randomization groups within the subgroups (eg, through stratified randomization), 13 (28.3%) entailed a prespecified subgroup analysis, and 1 (2.2%) was adjusted for multiple testing. Only 5 (10.9%) of the 46 subgroup findings had at least 1 subsequent pure corroboration attempt by a meta-analysis or an RCT. In all 5 cases, the corroboration attempts found no evidence of a statistically significant subgroup effect. In addition, all effect sizes from meta-analyses were attenuated toward the null. Conclusions and Relevance A minority of subgroup claims made in the abstracts of RCTs are supported by their own data (ie, a significant interaction effect). For those that have statistical support (P < .05 from an interaction test), most fail to meet other best practices for subgroup tests, including prespecification, stratified randomization, and adjustment for multiple testing. Attempts to corroborate statistically significant subgroup differences are rare; when done, the initially observed subgroup differences are not reproduced.
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Affiliation(s)
- Joshua D Wallach
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California2Meta-Research Innovation Center at Stanford (METRICS), Stanford University School of Medicine, Stanford, California
| | - Patrick G Sullivan
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California2Meta-Research Innovation Center at Stanford (METRICS), Stanford University School of Medicine, Stanford, California3Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - John F Trepanowski
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Kristin L Sainani
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California
| | | | - John P A Ioannidis
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California2Meta-Research Innovation Center at Stanford (METRICS), Stanford University School of Medicine, Stanford, California3Department of Medicine, Stanford University School of Medicine, Stanford, California4Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, California6Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, California
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Ioannidis JP. Hijacked evidence-based medicine: stay the course and throw the pirates overboard. J Clin Epidemiol 2017; 84:11-13. [DOI: 10.1016/j.jclinepi.2017.02.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Alturki R, Schandelmaier S, Olu KK, von Niederhäusern B, Agarwal A, Frei R, Bhatnagar N, Hooft L, von Elm E, Briel M. Premature trial discontinuation often not accurately reflected in registries: comparison of registry records with publications. J Clin Epidemiol 2017; 81:56-63. [DOI: 10.1016/j.jclinepi.2016.08.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2015] [Revised: 05/13/2016] [Accepted: 08/26/2016] [Indexed: 11/28/2022]
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van den Bogert CA, Souverein PC, Brekelmans CTM, Janssen SWJ, Koëter GH, Leufkens HGM, Bouter LM. Non-Publication Is Common among Phase 1, Single-Center, Not Prospectively Registered, or Early Terminated Clinical Drug Trials. PLoS One 2016; 11:e0167709. [PMID: 27973571 PMCID: PMC5156378 DOI: 10.1371/journal.pone.0167709] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 11/18/2016] [Indexed: 12/28/2022] Open
Abstract
The objective of this study was to investigate the occurrence and determinants of non-publication of clinical drug trials in the Netherlands.All clinical drug trials reviewed by the 28 Institutional Review Boards (IRBs) in the Netherlands in 2007 were followed-up from approval to publication. Candidate determinants were the sponsor, phase, applicant, centers, therapeutic effect expected, type of trial, approval status of the drug(s), drug type, participant category, oncology or other disease area, prospective registration, and early termination. The main outcome was publication as peer reviewed article. The percentage of trials that were published, crude and adjusted odds ratio (OR), and 95% confidence interval (CI) were used to quantify the associations between determinants and publication. In 2007, 622 clinical drug trials were reviewed by IRBs in the Netherlands. By the end of follow-up, 19 of these were rejected by the IRB, another 19 never started inclusion, and 10 were still running. Of the 574 trials remaining in the analysis, 334 (58%) were published as peer-reviewed article. The multivariable logistic regression model identified the following determinants with a robust, statistically significant association with publication: phase 2 (60% published; adjusted OR 2.6, 95% CI 1.1-5.9), phase 3 (73% published; adjusted OR 4.1, 95% CI 1.7-10.0), and trials not belonging to phase 1-4 (60% published; adjusted OR 3.2, 95% CI 1.5 to 6.5) compared to phase 1 trials (35% published); trials with a company or investigator as applicant (63% published) compared to trials with a Contract Research Organization (CRO) as applicant (50% published; adjusted OR 1.7; 95% CI 1.1-2.8); and multicenter trials also conducted in other EU countries (68% published; adjusted OR 2.2, 95% CI 1.1-4.4) or also outside the European Union (72% published; adjusted OR 2.0, 95% CI 1.0-4.0) compared to single-center trials (45% published). Trials that were not prospectively registered (48% published) had a lower likelihood of publication compared to prospectively registered trials (75% published; adjusted OR 0.5, 95% CI 0.3-0.8), as well as trials that were terminated early (33% published) compared to trials that were completed as planned (64% published; adjusted OR 0.2, 95% CI 0.1-0.3). The non-publication rate of clinical trials seems to have improved compared to previous inception cohorts, but is still far from optimal, in particular among phase 1, single-center, not prospectively registered, and early terminated trials.
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Affiliation(s)
- Cornelis A. van den Bogert
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, TB Utrecht, The Netherlands
- Central Committee on Research involving Human Subjects (CCMO), BH The Hague, the Netherlands
- National Institute for Public Health and the Environment (RIVM), Division of Public Health and Health Services, BA Bilthoven, The Netherlands
| | - Patrick C. Souverein
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, TB Utrecht, The Netherlands
| | - Cecile T. M. Brekelmans
- Central Committee on Research involving Human Subjects (CCMO), BH The Hague, the Netherlands
| | - Susan W. J. Janssen
- National Institute for Public Health and the Environment (RIVM), Division of Public Health and Health Services, BA Bilthoven, The Netherlands
| | - Gerard H. Koëter
- Central Committee on Research involving Human Subjects (CCMO), BH The Hague, the Netherlands
| | - Hubert G. M. Leufkens
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, TB Utrecht, The Netherlands
| | - Lex M. Bouter
- VU University Medical Center, Department of Epidemiology and Biostatistics, MB Amsterdam, the Netherlands
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Trampisch HJ, Rudolf H, Lange S. Heterogeneity of therapeutic effects. Atherosclerosis 2016; 255:124-125. [DOI: 10.1016/j.atherosclerosis.2016.10.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 10/19/2016] [Indexed: 11/26/2022]
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